Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex

UCSF UC San Francisco Previously Published Works

Title Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex.

Permalink https://escholarship.org/uc/item/8j92s7z1

Journal Cell reports, 31(1)

ISSN 2211-1247

Authors Werling, Donna M Pochareddy, Sirisha Choi, Jinmyung et al.

Publication Date 2020-04-01

DOI 10.1016/j.celrep.2020.03.053

Peer reviewed

eScholarship.org Powered by the California Digital Library University of California HHS Public Access Author manuscript

Author ManuscriptAuthor Manuscript Author Cell Rep Manuscript Author . Author manuscript; Manuscript Author available in PMC 2020 June 15. Published in final edited form as: Cell Rep. 2020 April 07; 31(1): 107489. doi:10.1016/j.celrep.2020.03.053.

Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex

Donna M. Werling1,2,32, Sirisha Pochareddy3,32, Jinmyung Choi3,32, Joon-Yong An1,4,5,32, Brooke Sheppard1, Minshi Peng6, Zhen Li3,7, Claudia Dastmalchi1, Gabriel Santpere3,8, André M.M. Sousa3, Andrew T.N Tebbenkamp3, Navjot Kaur3, Forrest O. Gulden3, Michael S. Breen9,10,11,12, Lindsay Liang1, Michael C. Gilson1, Xuefang Zhao13,14,15, Shan Dong1, Lambertus Klei16, A. Ercument Cicek17,18, Joseph D. Buxbaum9,10,11,19, Homa Adle- Biassette20, Jean-Leon Thomas21,22, Kimberly A. Aldinger23,24, Diana R. O’Day25, Ian A. Glass25, Noah A. Zaitlen26, Michael E. Talkowski13,14,15, Kathryn Roeder6,18, Matthew W. State1,27, Bernie Devlin16, Stephan J. Sanders1,27,33,*, Nenad Sestan3,28,29,30,31,* 1Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA 2Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA 3Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA 4Department of Integrated Biomedical and Life Science, Korea University, Seoul 02841, Republic of Korea 5School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Republic of Korea 6Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA 7Department of Neurosciences, University of California, San Diego, San Diego, CA 92093, USA

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). *Correspondence: [email protected] (S.J.S.), [email protected] (N.S.). AUTHOR CONTRIBUTIONS Conceptualization, K.R., M.W.S., B.D., S.J.S., and N.S.; Methodology, D.M.W., S.P., J.C., J.-Y.A., H.A.-B., J.-L.T., K.A.A., D.R.O., I.A.G., K.R., M.W.S., B.D., S.J.S., and N.S.; Software, D.M.W., J.-Y.A., C.D., L.L., M.C.G., and S.J.S.; Validation, D.M.W., S.P., J.C., J.-Y.A., C.D., S.D., B.D., S.J.S., and N.S.; Formal Analysis, D.M.W., S.P., J.C., J.-Y.A., B.S., M.P., G.S., M.S.B., L.K., A.E.C., B.D., and S.J.S.; Investigation, D.M.W., S.P., J.C., J.-Y.A., B.S., B.D., S.J.S., and N.S.; Resources, S.P., J.C., Z.L., C.D., A.M.M.S, A.T.N.T., N.K., F.O.G., L.L., M.C.G., X.Z., H.A.-B., J.-L.T., K.A.A., D.R.O., I.A.G., M.E.T., S.J.S., and N.S.; Data Curation, D.M.W., S.P., J.C., C.D., L.L., M.C.G., X.Z., S.D., and S.J.S.; Statistical analysis, D.M.W., J.C., J.-Y.A., B.S., M.P., M.S.B., B.D., and S.J.S.; Writing – Original Draft, D.M.W., S.P., J.C., J.-Y.A., B.S., B.D., S.J.S., and N.S.; Writing – Review & Editing, D.M.W., S.P., J.C., J.- Y.A., B.S., M.P., Z.L., C.D., G.S., A.M.M.S., A.T.N.T., N.K., F.O.G., M.S.B., L.L., M.C.G., X.Z., S.D., L.K., A.E.C., J.D.B., H.A.-B., J.-L.T., K.A.A., D.R.O., I.A.G., N.A.Z., M.E.T., K.R., M.W.S., B.D., S.J.S., and N.S.; Visualization, D.M.W., S.P., J.C., J.-Y.A., B.S., S.J.S., and N.S.; Supervision, J.D.B., N.A.Z., M.E.T., K.R., M.W.S., B.D., S.J.S., and N.S.; Project Administration, D.M.W., S.P., S.J.S., and N.S.; Funding Acquisition, K.R., M.W.S., B.D., S.J.S., and N.S. DECLARATION OF INTERESTS The authors declare no competing interests. SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j.celrep.2020.03.053. Werling et al. Page 2

8Neurogenomics Group, Research Programme on Biomedical Informatics, Hospital del Mar Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain 9Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 10Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 11Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 12Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 13Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA 14Department of Neurology, Harvard Medical School, Boston, MA 02115, USA 15Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA 02142, USA 16Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA 17Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey 18Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA 19Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 20Department of Pathology, Lariboisière Hospital, APHP, Biobank BB-0033-00064, and Université de Paris, 75006 Paris, France 21Department of Neurology, Yale University School of Medicine, New Haven, CT 06511, USA 22UMRS1127, Sorbonne Université, Institut du Cerveau et de la Moelle Épinière, 75013 Paris, France 23Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA 24Brotman Baty Institute for Precision Medicine, Seattle, WA 98195, USA 25Department of Pediatrics, University of Washington, Seattle, WA 98105, USA

26Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA 27Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA 28Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA 29Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA

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30Department of Comparative Medicine, Program in Integrative Cell Signaling and Neurobiology Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author of Metabolism, Yale School of Medicine, New Haven, CT 06510, USA 31Program in Cellular Neuroscience, Neurodegeneration, and Repair and Yale Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA 32These authors contributed equally to this work 33Lead Contact

SUMMARY Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both “constant” and “temporal-predominant” eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.

In Brief Werling et al. analyze gene expression across the span of human cerebral cortical development and profile the trajectories of individual genes, coordinated groups of genes, and their relationships to disorders. Integration of genetic variation identifies quantitative trait loci that implicate specific genes in loci associated with neuropsychiatric traits and disorders.

Graphical Abstract

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INTRODUCTION The human nervous system develops slowly over several decades, starting during embryogenesis and extending postnatally through infancy, childhood, adolescence, and young adulthood (Keshavan et al., 2014; Shaw et al., 2010; Silbereis et al., 2016; Tau and Peterson, 2010). Over this time, myriads of functionally distinct cell types, circuits, and regions are formed (Hu et al., 2014; Lui et al., 2011; Silbereis et al., 2016). To produce distinct structures and circuits, neural cells are born in an immature state and undergo a variety of molecular and morphological changes as they differentiate, migrate, and establish circuits. Consequently, the characteristics of a given cell and brain region at a given time offer only a snapshot of organogenesis and brain function, necessitating consistent profiling across development.

The molecular and cellular processes underlying development of the nervous system rely on the diversity of transcripts and their precise spatiotemporal regulation (Bae and Walsh, 2013; Silbereis et al., 2016). Functional genomic analyses of the developing human brain have revealed highly dynamic gene expression and epigenetic changes during prenatal and early postnatal development (Kang et al., 2011; Li et al., 2018) versus comparative stability over several decades of adulthood (Colantuoni et al., 2011; Jaffe et al., 2018; Kang et al., 2011; Li et al., 2018; Pletikos et al., 2014). Disruption of developmentally dynamic regulatory processes is likely to contribute to neurodevelopmental and neuropsychiatric disorders (Birnbaum and Weinberger, 2017; Breen et al., 2016; Geschwind and Flint, 2015; McCarroll

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and Hyman, 2013; Rosti et al., 2014; Sestan and State, 2018; Turner and Eichler, 2019). In Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author keeping with this expectation, spatiotemporal expression patterns have implicated mid-fetal brain development as a vulnerable process and the prefrontal cortex as a vulnerable region for autism spectrum disorder (ASD) and schizophrenia risk genes (Chang et al., 2015b; Gulsuner et al., 2013; Li et al., 2018; Network and Pathway Analysis Subgroup of the Psychiatric Genomics Consortium, 2015; Parikshak et al., 2013; Satterstrom et al., 2020; Willsey et al., 2013; Xu et al., 2014). More generally, atypical trajectories of brain maturation have been described in ASD, schizophrenia, and other neuropsychiatric traits and disorders (Birnbaum and Weinberger, 2017; Courchesne et al., 2007; Ecker et al., 2015; Insel, 2010; Keshavan et al., 2014; Shaw et al., 2010; Tang and Gur, 2018). Given that neuropsychiatric disorders have discrete ages of onset and progression and may arise because of genetic or environmental insults at various times during the life of an individual, there is a clear need to examine gene expression and neuropsychiatric risk across the span of human brain development.

In addition to spatiotemporal variation, genetic sequence variants also affect gene expression levels, which can contribute to differences in brain structure, function, and behavior (Elliott et al., 2018). Several laboratories and consortia have systematically identified such expression quantitative trait loci (eQTLs) in numerous tissues, including the brain (Akbarian et al., 2015; Dobbyn et al., 2018; Fromer et al., 2016; Gibbs et al., 2010, GTEx Consortium, 2015; Heinzen et al., 2008; Jaffe et al., 2018; Liu et al., 2010; Myers et al., 2007; Wang et al., 2018; BrainSeq: A Human Brain Genomics Consortium, 2015), but fewer include the developing human brain (Colantuoni et al., 2011; Jaffe et al., 2018; Kang et al., 2011; O’Brien et al., 2018; Walker et al., 2019). Therefore, developmentally regulated eQTLs are sparsely represented in the current catalog of human brain eQTLs, highlighting the need for additional resources. Such eQTL catalogs offer the potential to gain insight into the functional consequences of the hundreds of coding and noncoding genetic loci that have been associated with neuropsychiatric traits and disorders, including developmental delay, ASD, educational attainment, schizophrenia, major depressive disorder, and Alzheimer’s disease (Deciphering Developmental Disorders Study, 2017, Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014; Grove et al., 2019; Kosmicki et al., 2016; Lee et al., 2018; Sanders et al., 2015, 2017; Satterstrom et al., 2020).

To help fill this gap, we generated BrainVar, a unique resource of whole-genome sequencing (WGS) paired with bulk tissue RNA sequencing (RNA-seq) of 176 samples from the human dorsolateral prefrontal cortex (DLPFC) across development, from 6 post-conception weeks to young adulthood (20 years). We focused our analyses on the DLPFC because of its importance in higher-order cognition (Silbereis et al., 2016) and the observation that many risk genes for ASD and schizophrenia are co-expressed in the DLPFC during mid-fetal development (Gulsuner et al., 2013; Li et al., 2018; Network and Pathway Analysis Subgroup of the Psychiatric Genomics Consortium, 2015; Parikshak et al., 2013; Willsey et al., 2013). We present a systematic description of this resource, including demographics, gene expression across development, gene co-expression modules, and eQTLs. We describe interactions between these factors and comparisons with the BrainSpan dataset, cell-type- specific genes, and loci associated with neuropsychiatric traits and disorders. Our analysis replicates the late-fetal transition observation, a dramatic shift in gene expression between

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mid-fetal development and infancy (Li et al., 2018); refines the timing of this event; and Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author delineates the degree to which each gene is involved. We also identify 252,629 cis-eQTLs affecting 8,421 genes and classify their effects as prenatal-predominant, postnatal- predominant, or constant across brain development. Finally, we identify eQTLs that colocalize with genome-wide association study (GWAS) loci, linking specific genes to neuropsychiatric phenotypes.

RESULTS Description of the Cohort and Data Generation To characterize gene expression across prenatal and postnatal development of the human DLPFC and to identify genetic variants associated with expression changes, post mortem tissue was obtained from 176 de-identified, clinically unremarkable donors (genotypic sex: 104 male, 72 female) without known neuropsychiatric disorders or large-scale genomic abnormalities, ranging between 6 post-conception weeks and 20 years of age (Figure 1; Table S1). In keeping with prior analyses (Kang et al., 2011), we assign these samples to 12 developmental periods, which we group into four developmental epochs (Figures 1 and 2). Gene expression data were generated using RNA-seq from tissue dissected from the DLPFC (corresponding mainly to Brodmann area 46) or from the frontal cerebral wall (donors younger than 10 post-conception weeks). WGS data (31.5× median coverage) were generated simultaneously from DNA isolated from the same individuals.

Data Processing

RNA-seq reads were aligned and converted to log base 2 counts per million (log2CPM) per gene (STAR Methods), with 23,782 genes meeting minimum expression criteria. We restricted further analysis to these 23,782 cortically expressed genes, of which 16,296 (68.5%) encode proteins, whereas 7,486 (31.5%) are noncoding, including long noncoding RNA (lncRNA) (12.6% of total) and antisense (9.2% of total) genes (Table S2). For the 14 samples also profiled in BrainSpan (Li et al., 2018), gene expression was highly correlated per sample and per gene (Figure S1). In both datasets, the first principal component of gene expression is strongly correlated with developmental age (Figures 2A and S1). All samples were genotypically concordant between the WGS and RNA-seq data (Regier et al., 2018). Ancestry correlated strongly between principal-component analysis clusters and self-report (STAR Methods).

Temporal Dynamics of Gene Expression Prior analysis of the 40 brains in the BrainSpan cohort identified developmental age as the greatest source of between-sample variance in gene expression, especially during a “late- fetal transition” between 22 post-conception weeks and 6 postnatal months (Kang et al., 2011; Li et al., 2018). We replicate these findings in BrainVar. The first principal component explains 42% of the variance in gene expression and is highly correlated with developmental age (partial R2 = 0.88; Figures 2A and S2; similar results when excluding the 14 overlapping samples), with the greatest changes occurring in late fetal development and early infancy (Figure 2A).

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Using the increased resolution from the 176 brains in BrainVar, we show that the late-fetal Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author transition begins around 19 post-conception weeks (start of period 6) and that the most dramatic changes are complete by 6 postnatal months (end of period 8); we label this transitional phase as epoch 2 (Figure 2A). Considering the nine samples younger than 10 post-conception weeks (periods 1–2), we also observe an “early-fetal transition,” i.e., a coordinated shift in embryonic and early fetal development, which we label epoch 0 (Figure 2A).

To identify the specific genes that change in the late-fetal transition, we performed a trajectory analysis on the 167 samples in epochs 1–3; we excluded epoch 0 because of the sparse sampling before and during the early-fetal transition. Remarkably, over half of the genes expressed in the cortex exhibit a persistent, progressive, and statistically significant expression variance across this late-fetal transition (Figure 2B). We identified three distinct trajectories, with 6,934 “rising” genes (higher postnatal expression), 5,143 “falling” genes (higher prenatal expression), and 11,705 “non-transitional” genes (no statistically significant change). Considering more than three trajectories further split gene sets by variance rather than developmental profile (STAR Methods). Similar trajectories are observed for these three gene lists in the BrainSpan DLPFC data (Figure S2). Emphasizing the magnitude of this transition, the first principal component of the 11,705 non-transitional genes explains only 18.4% of the variance in gene expression and is weakly correlated with developmental age (partial R2 = 0.3; Figure S2).

The magnitude of the changes in individual genes’ expression levels across late-fetal transition can be estimated by calculating the difference in log2CPM expression between epoch 3 and epoch 1, ranging from 12.1 (OPALIN, a component of myelin) to −9.2 (IGF2BP1, an IGF2 binding protein); for context, the median epoch 3-to-epoch 1 changes in log2CPM values were 2.1, 0.1, and −1.1 for rising, non-transitional, and falling, respectively. The majority of changes in gene expression reflected relative amplification or attenuation of expression levels rather than binary presence/absence of expression, with only 621 rising genes and 95 falling genes specific to epoch 3 or 1 (defined as log2CPM ≤ −5 in the other epoch; Figure 2C; Table S2).

Characteristics of Transitional and Non-transitional Genes Compared with rising and non-transitional genes, falling genes had the highest median expression in epoch 1 (p < 2 × 10−16) and epoch 3 (p < 2 × 10−16, Wilcoxon rank-sum test [WRST]; Figure 2C) and the highest fraction of protein-coding genes (p < 2 × 10−16, Fisher’s exact test [FET]; Figure 2D) and were highly enriched for genes with high probability loss-of-function intolerant (pLI) scores (p = 5 × 10−11, WRST; Figure 2E). High pLI scores reflect detection of fewer protein-truncating variants than expected (Lek et al., 2016), suggesting that loss-of-function mutations in the gene are disfavored by natural selection (i.e., the gene is haploinsufficient). Rising genes had a similar proportion of protein-coding genes as falling genes (p = 0.94, FET; Figure 2D) but were depleted for genes with high pLI scores (p = 1 × 10−7, WRST; Figure 2E). If the timing of a gene’s highest expression corresponds to the timing of its most critical functions, then the pLI

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difference between falling and rising genes suggests that prenatal development is especially Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author sensitive to haploinsufficiency.

Compared with RNA-seq data from 53 adult tissues (GTEx Consortium, 2015), falling genes were only enriched in non-cortical tissues (driven by genes related to RNA transcription and cell division; Table S2), whereas rising genes were enriched for many brain regions, including the adult cortex and excluding cerebellum (Figure 2F), highlighting the distinctions between the fetal and adult cortex. Non-transitional genes had the lowest proportion of protein-coding genes and were expressed ubiquitously across adult tissues (Figure 2).

Cell Type Dynamics across Development To capture the contribution of changing cell type proportions to gene expression profiles, we assessed expression trajectories of genes specific to each of ten cortical cell types from prenatal (Nowakowski et al., 2017) and postnatal human brain (Li et al., 2018; Velmeshev et al., 2019). The estimated profiles of all ten cell types vary dramatically across epoch 2, with radial glia/neural progenitor cells and fetal neurons decreasing as mature neurons and other glial cells increase (Figures 2G and 2H); this pattern is replicated in BrainSpan DLPFC samples (Figure S2). These analyses support the hypothesis that varying cell type proportions are major contributors to the late-fetal transition in the DLPFC (Li et al., 2018), but distinguishing cellular composition effects from differential expression within a cell type will require single-cell data from across this age range.

Co-expression Modules in the Developing Human Cortex Tofurthercharacterizetherelationshipsbetweenthe23,782cortically expressed genes, we applied a weighted gene co-expression network analysis (WGCNA) (Langfelder and Horvath, 2008) to define 19 consensus modules that included 10,459 genes (Figures 3A and S3; Table S3). As expected, genes within each module shared functional roles (Figure 3B), temporal trajectories of geneexpression(Figures3C and3D),regulatory transcriptionfactors (Figure S3), and cell type enrichment (Figures 3E and 3F). Module preservation analysis using BrainSpan data (Li et al., 2018) identified similar co-expression patterns across brain regions, especially independent DLPFC samples (Figure 3G).

Similar to transitional genes (Figure 2), multidimensional scaling of the module eigengenes demonstrated that developmental age accounted for 44.7% and 36.1% of the variance in the first two dimensions. Considering the position of the 19 modules along these two dimensions and the developmental trajectories of the genes in each module, we identified five groups of related modules (Figures 3A and 3B). Group 1 modules (M1 black, M2 royal blue, M3 green-yellow, and M4 yellow) are enriched for falling genes, whereas group 5 modules (M16 blue, M17 silver, M18 light cyan, and M19 turquoise) are enriched for rising genes (Figure 3D). The remaining three groups (2, 3, and 4) are enriched for non-transitional genes (Figure 3D).

Five modules are of particular note. The M2 royal blue module (group 1) captures cell cycle ontology and is enriched in neuroprogenitor cells, radial glia, and intermediate progenitor cells. The M4 yellow module (group 1) is enriched for numerous ontology terms related to

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neuronal development, contains genes specific to neuronal stem cells (e.g., NCAM1/ncam Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author and PROM1/cd133), and is highly enriched for genes related to maturing excitatory and inhibitory neurons. The M8 red module (group 3) is enriched for ontology terms relating to cell fate and morphogenesis, is highly enriched for noncoding genes, and has an expression peak in early fetal development, capturing many genes that are involved in early-fetal transition (Figure 2A). Several genes associated with regional patterning in non-cortical tissues, including the hindbrain (e.g., UNCX and CCDC140) and hypothalamus (e.g., DMBX1 and SOX14), are expressed at high levels in this module. The M18 light cyan and M19 turquoise modules (group 5) are strongly enriched in glial and other nonneuronal cell clusters; accordingly, both modules are enriched for ontology terms related to immune responses. The M19 turquoise module is also enriched in excitatory neurons in the postnatal cortex and ontology terms relating to synaptic signaling and neurotransmitter transport.

Intersection of Developmental Expression with Human Traits and Disorders We next considered the intersection between genes associated with developmental trajectories, modules, or cell types and genes associated with ten human traits and disorders. For ASD and developmental delay with and without seizures, we used gene lists derived from exome association studies of rare and de novo variants (Deciphering Developmental Disorders Study, 2017; Heyne et al., 2018; Satterstrom et al., 2020). For educational attainment, attention deficit hyperactivity disorder (ADHD), schizophrenia, major depressive disorder, multiple sclerosis, Parkinson’s disease, and Alzheimer’s disease, we used genes within 10 kb of the lead SNP detected in GWASs (Chang et al., 2017; Demontis et al., 2019; Beecham et al., 2013; Lambert et al., 2013; Lee et al., 2018; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014; Wray et al., 2018). For our analyses, we excluded genes within the major histocompatibility complex on chromosome 6 because of the complicated nature of this region (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014).

Developmental delay, ASD, and educational attainment genes were enriched for falling genes (p = 8.5 × 10−6, p = 5.1 × 10−3, and p = 2.4 × 10−4 respectively; FET adjusted for 30 comparisons), consistent with a prenatal origin for aspects of their neurobiology. A non- significant trend toward enrichment for rising genes was observed for Parkinson’s disease and Alzheimer’s disease (Figure 4A; Table S4). The M4 yellow module was enriched for ASD and educational attainment genes, including NRXN1, TCF4, and BCL11A (Figure 4B; Table S4), and the M9 brown module (enriched for chromatin organization Gene Ontology terms and non-transitional genes; Figure 3) was enriched for genes associated with developmental delay and educational attainment, including CDK13, PACS1, and EP300 (Figure 4B; Table S4).

Across the ten CNS traits and disorders, five cell type clusters (C) (Li et al., 2018; Nowakowski et al., 2017) showed significant enrichment in fetal brain (Figure 4C; Table S4) and none in adult brain (Figure S4; Table S4). ASD genes were enriched for C18 excitatory newborn neurons and C1 striatal interneurons (Figure 4C), in keeping with a role of excitatory and inhibitory lineages (Satterstrom et al., 2020). Both lineages were also enriched in educational attainment, specifically C3 early excitatory neurons and C6 medial

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ganglionic eminence (MGE)-derived interneurons, whereas genes associated with Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author developmental delay with seizures were enriched in C15 caudal ganglionic eminence (CGE)-derived interneurons (Figure 4C). We observed a nominally significant trend toward enrichment of C19 microglia genes in multiple sclerosis and Alzheimer’s disease.

Common Genetic Variants Regulating Gene Expression We identified 6,573,196 high-quality SNPs and insertions or deletions (indels) from the WGS data using methods described previously (Werling et al., 2018), with an allele frequency of at least 5% in our prenatal (periods 1–6, n = 112) and postnatal (periods 8–12, n = 60) samples (Figure 1). To identify eQTLs within 1 Mb of a gene (eGene), we used linear regression for adjusted expression level (STAR Methods), with developmental period, sex, and the first five principal components for ancestry as covariates. Results were corrected for multiple comparisons using Benjamini-Hochberg (false discovery rate [FDR] ≤ 0.05). To distinguish temporal-predominant eQTLs, we performed three cis-eQTL analyses: all 176 samples (complete sample, 216,026 eQTLs of 5,728 eGenes), 112 prenatal samples (periods 1–6, 154,440 eQTLs of 4,378 eGenes), and 60 postnatal samples (periods 8–12, 51,528 eQTLs of 2,199 eGenes). These discovery rates are in line with similarly sized cohorts (Figure S5). The union of these three analyses identified 252,629 eQTLs of 8,421 eGenes (Table S5). As expected, the eQTLs are enriched for markers of active transcription derived from the human brain (Figure S5; Li et al., 2018; Reilly et al., 2015; Kundaje et al., 2015). We find that eQTL effect size and direction are correlated with prenatal whole brain (O’Brien et al., 2018) (Pearson’s r = 0.73, p ≤ 1 × 10−16; Figure 5A) and postnatal (adult) frontal cortex (Aguet et al., 2017) (Pearson’s r = 0.73, p ≤ 1 × 10−16; Figure 5A) from independent datasets.

Temporal Predominance of eQTLs We leveraged the consistently processed prenatal and postnatal data in BrainVar to identify eQTLs with differing effect sizes across development (Figure 5B; STAR Methods). The majority of eQTLs were constant, reaching nominal significance in all three analyses with the same direction of effect (161,923 eQTLs, 64.1% of the total). Many eQTLs were prenatal-predominant, with significantly greater prenatal than postnatal effect sizes (24,760 eQTLs, 9.8%). Fewer eQTLs were postnatal-predominant, with significantly greater postnatal than prenatal effect sizes (9,352 eQTLs, 3.7%). The remaining 56,593 eQTLs (22.4%) showed a trend toward stronger prenatal effects (19.8%) or postnatal effects (2.6%). With larger sample sizes, we would expect a greater fraction of constant eQTLs to show some degree of temporal specificity, especially postnatal. Although the magnitude of effect varied across development for many eQTLs, we did not observe a single eQTL with opposing prenatal and postnatal directions of effect.

Temporal Predominance of eGenes Most eGenes have more than one eQTL (5,538 of the 8,421, 65.8%). Defining the top eQTL per eGene as that with the lowest FDR-significant p value in any of the three sample sets (Table S5), we identified 2,977 (35.4% of total) constant eGenes, 1,691 (20.1%) prenatal- predominant eGenes, and 1,145 (13.6%) postnatal-predominant eGenes (Figure 5B). The remaining 2,608 eGenes (31.0%) trend toward prenatal (25.1%) or postnatal (5.9%) effects.

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Because of linkage disequilibrium (LD), cis-eQTLs for an eGene are likely to have a similar Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author direction and magnitude of effect; accordingly, the temporal category of the top eQTL matched the majority of eQTLs for 88.2% of all eGenes (7,425 eGenes; Figure S5).

To validate the prenatal- and postnatal-predominant eQTLs, we evaluated their performance in independent datasets. In prenatal whole brain (O’Brien et al., 2018), we observed stronger correlation for the effects of prenatal-predominant (r = 0.54, p = 3.9 × 10−22) than postnatal- predominant eGenes (r = 0.36, p = 1.1 × 10−3; Figure 5C). In contrast, in postnatal frontal cortex (Aguet et al., 2017), stronger correlations were observed for the effects of postnatal- predominant (r = 0.60, p = 4.0 × 10−24) than prenatal-predominant eGenes (r = 0.37, p = 1.1 × 10−6;Figure 5C).

Characteristics of Genes Influenced by eQTLs The top eQTL for constant eGenes is closer to the transcription start site than that for temporal-predominant eGenes (median: 92,223 bp versus 403,702 bp, p ≤ 2 × 10−16, two- sided WRST; Figure 5D). However, −log10(P) values increased with proximity to the transcription start site for constant and temporal-predominant eQTLs (for both sets, p ≤ 2 × 10−16, linear regression), as did eQTL effect size to a small degree (constant p = 1.0 × 10−12, temporal-predominant p = 0.03, linear regression). Compared with constant eGenes, temporal-predominant eGenes also included a higher proportion of protein-coding genes (odds ratio [OR] = 1.56, 95% confidence interval [CI]: 1.40–1.74), p = 3.7 × 10−16, two- sided FET; Figure 5E), genes with high pLI scores (pLI ≥ 0.995; OR = 1.45 [95% CI: 1.12– 1.90], p = 0.004, two-sided FET; Figure 5F), and greater connectivity in protein-protein interaction (PPI) networks (median Z score 1.45 versus 0.91, p ≤ 2 × 10−16, two-sided WRST;Figure 5H).

Given the dynamic expression profiles over development (Figure 2), we expected prenatal- predominant eGenes to be enriched for falling genes and postnatal-predominant eGenes to be enriched for rising genes. We did not observe these effects (Figure 5I). Instead, the prenatal-predominant eGenes are enriched for rising genes (OR = 1.5 [95% CI: 1.3–1.6], p = 1.1 × 10−10, two-sided FET; for example, Figure 5J), and pre- and postnatal-predominant eGenes are depleted for falling genes (prenatal OR = 0.79 [95% CI: 0.69–0.9], p = 4.2 × 10−4; postnatal OR = 0.85 [0.73–0.997], p = 0.04; two-sided FET). Instead, we observed coordination between the timing of eQTLs’ strongest effects and the timing of eGenes’ greatest expression variation between samples (STAR Methods). Prenatal-predominant eGenes are strongly enriched for genes with greater prenatal variance (OR = 2.3, p = 4.0 × 10−52, two-sided FET) and depleted for genes with greater postnatal variance (OR = 0.36, p = 1.6 × 10−15, two-sided FET). Postnatal-predominant eGenes show a complementary but weaker pattern of enrichment for genes with greater postnatal variance (OR = 1.1, p = 0.25, two-sided FET) and depletion of genes with greater prenatal variance (OR = 0.76, p = 7.0 × 10−5, two-sided FET). Considering the role of selective pressure, we observed that genes with higher pLI scores also had lower eQTL effect sizes (p = 4.0 × 10−36; two-sided WRST; Figure 5J) as well as lower expression variance between samples (Figure 5K).

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eQTLs in Human Traits and Disorders Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author The differences in constant and temporal-predominant eGenes led us to consider how genes associated with neuropsychiatric traits and disorders (Figure 4) fit into this classification. Gene sets associated with traits by GWAS loci or exome sequence association followed the patterns of temporal-predominant eGenes but to a greater extent, with a higher proportion of protein-coding genes (Figure 5E), higher pLI scores (Figure 5F), and stronger clustering within PPI networks (Figure 5H).

At the variant level, we expect GWAS loci to be enriched for eQTLs in relevant tissues (Fromer et al., 2016; Nicolae et al., 2010). Using a permutation-based method accounting for LD structure, minor allele frequency (MAF), and gene density (STAR Methods), we tested four of the larger GWASs and observed eQTL enrichment for educational attainment, schizophrenia, and multiple sclerosis but not Alzheimer’s disease (Figure S6; Table S6). We did not see evidence of the reverse hypothesis that eGenes are enriched for GWAS signals (Figure S6). Using a colocalization analysis, we looked for overlap between specific eQTL loci with educational attainment and schizophrenia GWAS loci using a posterior probability of colocalization threshold of 0.8. In the schizophrenia GWAS, 13 of 108 loci (12.0%) showed evidence of colocalization, including two prenatal-predominant and two postnatal- predominant eQTLs (Table S6). A lower proportion of educational attainment loci showed evidence of colocalization (4.1%, 52 of 1,271), including 14 prenatal-predominant and two postnatal-predominant eQTLs (Table S6). Focusing on multigenic loci with the strongest evidence of colocalization, we implicate specific genes and expression changes as the likely mechanism underlying the GWAS loci. SNPs associated with educational attainment at a chromosome 14 locus (Figure 6A) colocalized only with eQTLs for the lncRNA LOC101926933 (also called RP11–298I3.1, AL132780.1, or ENSG00000257285; Figure 6B). Across the locus, the p values for the GWAS SNPs and LOC101926933 eQTLs are highly correlated, resulting in a posterior probability for colocalization of 0.92 (Figure 6C); for this locus, eQTL effect size is similar across development (Figure 6D). We also observe colocalization of prenatal-predominant eQTLs with educational attainment or schizophrenia GWAS loci. For example, SNPs contributing to an educational attainment GWAS locus on chromosome 12 (Figure 6E) overlap specifically with eQTLs for the protein-coding gene RHEBL1, which encodes a brain-enriched G-protein activator of the mTOR pathway (Figure 6F). GWAS and RHEBL1 eQTL prenatal p values are highly correlated and result in a posterior probability for colocalization of 0.97 (Figure 6G). We see a significantly greater eQTL effect size in prenatal compared with postnatal samples (p = 0.003; Figure 6H), with higher RHEBL1 expression associated with increased educational attainment.

DISCUSSION In this manuscript, we describe BrainVar, a unique resource of paired genome (WGS) and transcriptome (bulk tissue RNAseq) data derived from 176 human DLPFC samples across prenatal and postnatal development (Figure 1). We identify 23,782 genes expressed during human cortical development, gene lists relating to developmental trajectories and co- expression, and common variants that alter gene expression (eQTLs). Our analyses show how these datasets relate to each other and to gene expression in cell types derived from

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single-cell RNA-seq data and to CNS traits and disorders derived from genomic analyses Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author (exome sequencing and GWAS). In addition to developing a resource with utility for future studies of human development, neurobiology, and neuropsychiatric disorders, we also describe key biological insights, including the nature of the late-fetal transition in gene expression (Figures 2 and 3), an early-fetal transition (Figures 2 and 3), developmental processes and cell types implicated in CNS traits and disorders (Figure 4), eQTLs split by effect size across development (Figure 5), differing characteristics of genes with constant versus temporal-predominant eQTLs (Figure 5), and the application of this dataset to implicate specific genes at GWAS loci (Figure 6).

Principal component analysis identifies developmental age as the most important factor underlying the variance in gene expression in this dataset. The majority of this temporal variance occurs in two transitional phases (Figure 2), the early-fetal and late-fetal transitions. The early-fetal transition is a coordinated decrease in expression of multiple genes in early development (epoch 0; periods 1–2; 6–10 post-conception weeks) that coincides with the establishment of regional identity across the brain. Concordant with a possible role of the early-fetal transition in this process, the expression of several genes associated with non- cortical tissues (e.g., UNCX and DMBX1) is decreased during this period. In addition, we found that the early-fetal transition is captured in the M8 red module (Figure 3), which is enriched for lncRNA transcripts and Gene Ontology terms related to morphogenesis and cell fate.

The late-fetal transition between mid-fetal development and infancy involves over 12,000 genes with similar numbers rising and falling (Figure 2). Prior reports of humans (Li et al., 2018) and primates (Zhu et al., 2018) associated this transition with a reduction in intra- and inter-regional variation evident at the levels of bulk tissue and individual cell types. Our data similarly suggest that this transition represents a combination of changes in the relative proportions of various cell types and biological processes within these cells (Figures 2 and 3). Critically, the larger BrainVar sample set allowed us to define 19 post-conception weeks as the inflection point at which the late-fetal transition begins (Figure 2), further distinguishing the late-fetal transition from previously reported organotypic changes (Domazet-Loso and Tautz, 2010; Kalinka et al., 2010; Li et al., 2018).

Although previous analyses have identified eQTLs in human brain tissue postnatally (Fromer et al., 2016; GTEx Consortium, 2015) and prenatally (Jaffe et al., 2018; O’Brien et al., 2018; Walker et al., 2019), no prior study has assessed the effect of genomic variation on gene expression across the whole of brain development, from embryogenesis through fetal development, infancy, childhood, and adolescence and into young adulthood. Consequently, we were able to identify temporal-predominant eQTLs that have a greater effect on expression prenatally or postnatally (Figure 5B). The eQTLs identified here were highly correlated with prior eQTL catalogs (Aguet et al., 2017; O’Brien et al., 2018) despite differing cohorts, methods, and analysis (Figures 5A and 5B). Furthermore, comparison with these independent catalogs support our temporal categorization of eQTLs, with prenatal- predominant eQTL effects more correlated in prenatal whole-brain and postnatal- predominant eQTL effects more correlated in the postnatal frontal cortex (Figure 5C).

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Across multiple metrics, we observe dramatic differences between eGenes with constant and Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author temporal-predominant eQTLs (Figures 5D–5G). Compared with other genes expressed in the cortex, genes affected by constant eQTLs are more likely to be noncoding and have low pLI scores and few protein-protein interactions. In contrast, genes regulated by eQTLs with a degree of temporal specificity are similar to genes for which we did not detect eQTLs. Critically, we find that pLI score, a measure of sensitivity to variation in genetic sequence, and eQTL effect size are inversely related (Figure 5J). Furthermore, prenatal-predominant eGenes are more common among rising genes, which have their highest expression during postnatal time. These observations suggest that developmental and evolutionary constraints limit the population frequency or effect of eQTLs on key developmental processes, a hypothesis that might be testable in future studies as additional information concerning spatiotemporal and cell type specificity of enhancers and eQTLs becomes available for a variety of tissues. Under this model, constant eQTLs with high effect sizes tend to influence genes that tolerate variation in expression (e.g., non-rate-limiting metabolic steps) or are non-critical to brain function, whereas temporal-predominant eQTLs tend to influence genes with critical roles that are sensitive to variation in genetic sequence but only to a small degree or at a stage in development when variation in expression of the gene is tolerated.

The eQTLs identified here also provide insights into CNS traits and disorders, with colocalization in 13 of 108 GWAS loci for schizophrenia and 52 of 1,271 GWAS loci for educational attainment, including the lncRNA LOC101926933 and the protein-coding gene RHEBL1 (Figure 6). LOC101926933 remains largely uncharacterized, whereas RHEBL1 (Ras homolog enriched in brain-like 1) is a highly conserved G-protein that activates mTOR (Bonneau and Parmar, 2012), a pathway that has been implicated previously in neurodevelopmental and neurodegenerative disorders (Lipton and Sahin, 2014). Our results suggest that higher expression of RHEBL1, which may lead to greater mTOR activation, is associated with increased educational attainment. Of note, RHEBL1 has a pLI score of 0, suggesting that loss of one allele does not lead to a selective disadvantage. Higher-resolution datasets across development, including single cells, additional brain regions, and larger sample sizes, along with complementary analyses of brains of individuals with neuropsychiatric disorders and rare genetic disorders, are likely to provide additional insights. The combination of genomic and transcriptomic data across development allows us to interrogate human cortical development from a molecular perspective at a higher resolution than before. Understanding patterns of temporal and cell type specificity, along with eQTL colocalization to resolve GWAS loci, has already provided insights into the pathology underlying neuropsychiatric disorders. Further delineation of these patterns is likely to be critical for a detailed understanding of etiology as a foundation for therapeutic development.

STAR★METHODS LEAD CONTACT AND MATERIALS AVAILABILITY Further information and requests for resources and reagents should be directed to and will be fulfilled by Stephan Sanders ([email protected]).

This study did not generate new unique reagents.

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EXPERIMENTAL MODEL AND SUBJECT DETAILS Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author This study was conducted using postmortem human brain specimens from tissue collections at the Department of Neuroscience at Yale University School of Medicine. Additional specimens were procured from the Birth Defects Research Laboratory at the University of Washington, Advanced Bioscience Resources Inc., Human Brain Collection Core (HBCC), the Brain and Tissue Bank at the University of Maryland, the MRC-Wellcome Trust Human Developmental Biology Resource at the Institute of Human Genetics, University of Newcastle, UK, and the Human Fetal Tissue Repository at the Albert Einstein College of Medicine (AECOM). Tissue was collected after obtaining parental or next of kin consent and with approval by the institutional review boards at the Yale University School of Medicine, the National Institutes of Health, and at each institution from which tissue specimens were obtained. Tissue was handled in accordance with ethical guidelines and regulations for the research use of human brain tissue set forth by the NIH (https:// oir.nih.gov/sites/default/files/uploads/sourcebook/documents/ethical_conduct/guidelines- biospecimen.pdf and the WMA Declaration of Helsinki (https://www.wma.net/policies-post/ wma-declaration-of-helsinki-ethical-principles-for-medical-researchinvolving-human- subjects/).

All available non-identifying information was recorded for each specimen in Table S1. In total, 176 postmortem brain specimens (104 male, 72 female; postmortem interval of 21.7 ± 15.9 (mean ± SD) hours and pH, 6.41 ± 0.35) ranging in age from 6 post-conception weeks to 20 postnatal years (Figure 1; Table S1) were included in this study. Fetal age was extrapolated based on the date of the mother’s last menstruation, characteristics of the fetus noted upon ultrasonography scanning, foot length of the fetus, and visual inspection. The postmortem interval (PMI) was defined as hours between time of death and time when tissue samples were frozen.

METHOD DETAILS Tissue dissection—Tissue was dissected as described previously (Kang et al., 2011). Samples collected from 6 – 9 post-conception weeks specimens contained the entire thickness of the cerebral wall. Samples collected from 12 to 22 post-conception weeks specimens contained the cortical plate. Samples from 35 post-conception weeks to 20 postnatal years were dissected such that the entire gray matter (layer 1–6) and part of the underlying subplate (4 – 12 postnatal months) or white matter (1 – 20 postnatal years) were collected.

RNA extraction and quality assessment—Total RNA was extracted using mirVana kit (Ambion) with some modificationsto the manufacturer’s protocol, as described below. Each tissue sample was pulverized with liquid nitrogen in a prechilled mortar and pestle and transferred to a chilled safe-lock microcentrifuge tube (Eppendorf). Per tissue mass, equal mass of chilled stainless-steel beads (Next Advance, catalog # SSB14B) along with one volume of lysis/binding buffer were added. Tissue was homogenized for 1 min in Bullet Blender (Next Advance) and incubated at 37°C for 1 min. Another nine volumes of the lysis/ binding buffer were added, homogenized for 1 min, and incubated at 37°C for 2 min. One- tenth volume of miRNA Homogenate Additive was added, and extraction was carried out

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according to the manufacturer’s protocol. RNA was treated with DNase using TURBO Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author DNA-free Kit (Ambion/Life Technologies) and RNA integrity was measured using Agilent 2200 TapeStation System.

RNA-seq library preparation and sequencing—Barcoded libraries for RNA-seq were prepared with 5ng of RNA using TruSeq Stranded Total RNA HT Sample Prep Kit with Ribo-Zero Gold kit (Illumina) per manufacturer’s protocol. Paired-end sequencing (100 bp × 2) was performed on HiSeq 4000 sequencers (Illumina) at Yale Center for Genome Analysis.

DNA extraction—Genomic DNA was isolated using the QIAamp DNA Mini Kit (QIAGEN). In detail, approximately 25 mg of brain tissue was transferred to a chilled safe- lock microcentrifuge tube (Eppendorf) and equal mass of chilled stainless-steel beads (Next Advance, catalog # SSB14B) along with 90 μl of buffer ATL were added. Tissue was homogenized for 1 min in Bullet Blender (Next Advance) and incubated at 37°C for 1 min. Another 90 ul of buffer ATL was added and blended for an additional minute. After incubation on ice for 5 min, tubes were gently centrifuged to collect beads at the bottom. Supernatant was transferred to a new tube and 20 μl of Proteinase K was added. Sample was incubated at 56°C for 3 hours in a shaking heat block. After incubation, genomic DNA was further purified following the manufacturer’s protocol. DNA was eluted in nuclease free water and concentration was estimated by nanodrop.

Whole-genome sequencing—DNA library preparation and sequencing were carried out at GENEWIZ (New Jersey). Before library preparation, the concentration of the DNA was measured using a fluorescent assay and DNA quality was assessed by visualization on agarose gels. PCR-free DNA library preparation was performed and resulting libraries were sequenced at 2×150 bp to achieve mean coverage of 30x (Table S1).

QUANTIFICATION AND STATISTICAL ANALYSIS WGS variant calling—Using the pipeline from the Centers for the Common Disease Genomics project (Regier et al., 2018), FASTQ reads were aligned to the GRCh38 reference from the 1000 Genomes Project using BWA-MEM version 0.7.15 (Li and Durbin, 2009). Reads were sorted and duplicates were removed with Picard, version 2.17.5 (https:// github.com/broadinstitute/picard/); base quality score recalibration was then performed with the Genome Analysis Toolkit (GATK), v3.8–0-ge9d806836 (McKenna et al., 2010). Variant calling and joint genotyping were done with the Haplotyper and Genotyper tools from Sentieon v201711.01, a toolkit containing modules that are mathematically equivalent to their counterparts in the GATK (Freed et al., 2017). SNP and indel recalibration were performed on the joint genotyped VCF file. Variant Quality Score Recalibration (VQSR) metrics were created from a training set of highly validated variant resources: dbSNP build 138, HapMap 3.3, 1000 Genomes OMNI 2.5, and 1000 Genomes Phase 1. For the following analyses, we excluded: variant calls with any VQSR tranches (keeping “PASS” only), variants located in low-complexity regions (Li, 2014), variants located on non-canonical chromosomes (decoy chromosomes or contigs), indels > 50 bp, single nucleotide variants (SNVs) with allele balance > 0.78 or < 0.22 (indels > 0.8 or < 0.2), variants with < 90% call

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rate, and variants and genotypes that did not meet high quality thresholds as identified in an Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author ROC-based optimization procedure using family-based WGS data (Werling et al., 2018).

Variants with a minor allele frequency of ≥ 5% in both the prenatal (periods 1–6; N = 112) and postnatal (periods 8–12; N = 60) samples and Hardy Weinberg equilibrium p value ≥ 1×10−12, were included in downstream expression quantitative trait locus (eQTL) analysis (N = 6,573,196 variants). For annotation and subsequent analyses, we converted the final VCF into Variant Dataset Format using Hail version 0.1. SNPs and insertions/deletions (up to 50bp) annotation based on the GENCODE comprehensive version 21 (Harrow et al., 2012) using Ensemble VEP version 90 (McLaren et al., 2016).

RNA-seq alignment and gene-level read count quantification—RNA-seq reads were aligned to the human genome (hg38/GRCh38) using STAR aligner (Dobin et al., 2013) and gene-level read counts were calculated using HTSeq (Anders et al., 2015) based on GENCODE v21 annotation (Harrow et al., 2012).

Read counts per gene were then converted to counts per million (CPM), which were logarithmically scaled to base 2 (log2CPM). Of the 60,155 genes assessed, 23,782 were defined as being cortically expressed, based on CPM ≥ 1 in at least 50% of samples of either sex in at least one of the 12 developmental periods (Table S2). For these 23,782 genes, the median log2CPM ranged from −5.9 to 12.3, with a median of 2.3.

RNA-seq normalization and technical artifact correction—The read count data matrix of 176 samples by 60,155 genes (Table S2) was normalized as follows:

• Step 1: Read count data matrix converted to counts per million (CPM). • Step 2: Genes with CPM ≥ 1 CPM in at least 50% of the samples in any one sex in any one period were included; 23,782 genes passed these criteria.

• Step 3: CPM values were transformed to log2(CPM) using the voom function in the limma R package (Law et al., 2014; Ritchie et al., 2015).

• Step 4A: In order to correct for the technical artifacts, we performed hidden covariate analysis on the residuals of the expression matrix after developmental period and sex were subtracted from the log2(CPM) data matrix using the hidden covariate analysis method (HCP) (Mostafavi et al., 2013).

• Step 4B: In parallel, we performed surrogate variable analysis (SVA) on residuals of the expression matrix after developmental period and sex were subtracted from the log2(CPM) data matrix using the SVA R package (Leek et al., 2012).

• Step 5: In each of Step 4A and 4B, we subtracted contributions from 20 hidden covariates (HCP) and 2 surrogate variables (SVA) from the log2(CPM) data matrix.

Unadjusted log2CPM gene expression data for 176 samples by 23,782 genes was used for expression trajectory (Figure 2) and WGCNA analyses (Figure 3), while the adjusted (HCP/ SVA) values were used for eQTL analysis.

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DATA QUALITY AND SAMPLE IDENTITY ASSESSMENT Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author To confirm that the WGS and RNA-seq data from each sample were of sufficient quality for downstream analysis and corresponded to the same individual, a series of quality metrics and checks were performed (Table S1).

For the WGS data, coverage metrics were assessed using PicardTools (v2.18.1). Mean coverage per sample ranged from 22.7–65.4x, with a cohort median of 31.5x. Across all samples, 92.1%–93.6% of the mapped genome was covered at 10x or greater (mean of 92.8%). The FREEMIX metric from VerifyBamId (v1.1.3; Jun et al., 2012) was used to identify samples with potential contamination, with a maximum observed FREEMIX score of 0.064, suggesting no contamination (Lek et al., 2016).

Sample identity was verified by comparing sex and genotype between the WGS and RNA- seq data. In the WGS data, sex was determined from chromosome X heterozygosity using Peddy (v0.3.2; (Pedersen and Quinlan, 2017)), with the Peddy hg19.sites converted to GRCh38 using the UCSC Genome Browser LiftOver utility. High-quality variants with an allele frequency ≥ 1% were exported from the VCF using Hail for input into Peddy. In the RNA-seq data, sex was determined from the expression levels of XIST and the 18 most highly expressed genes on chromosome Y: KDM5D, DDX3Y, ZFY, TBL1Y, PCDH11Y, PRKY, USP9Y, RPS4Y1, TXLNGY, NLGN4Y, TTTY14, UTY, EIF1AY, GYG2P1, TTTY10, TTTY15, KALP. Based on gene-specific expression thresholds determined by visual inspection of bi-modal expression histograms, each sample’s sex was predicted according to the expression level of all 19 genes. Sex was consistent in the WGS and RNA- seq data for all 176 samples and matched the recorded sex in 132 out of 134 samples with such data (55/56 females, 77/78 males).

To confirm identity by genotype, we compared the genotypes from 289 common, coding SNPs with high fixation index (FST) (Sanders et al., 2015), called from both the WGS and RNA-seq data. Genotypes were callable in both data types for 118–206 SNPs per sample (40.8%–71.3% of 289 SNPs; median = 177, 61.2%). SNP variant genotypes were highly concordant between the WGS and RNA-seq data (median 87.4% concordance between WGS and RNA-seq for the corresponding sample; lowest concordance 73%), with corresponding samples showing higher concordance than comparisons between all discordant samples. There was no evidence of duplicate or closely related samples (SNP- based relatedness coefficients from Peddy: −0.000332 to 0.1481).

To confirm the approximate accuracy of samples’ reported age, the expression level of the doublecortin gene (DCX) was examined. DCX is involved in neuron migration, and is expressed most strongly during prenatal development, with distinctly decreased postnatal expression. All 176 samples showed the expected DCX expression levels given samples’ reported age. Similar results across all expressed genes were obtained using principal component analysis (below).

Ancestry estimation—Ancestry was estimated using principal component analysis of common SNPs and indels in the WGS data, run alongside 3,804 additional individuals of known ancestry with WGS data (parents from the Simons Simplex Collection; An et al.,

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2018). From 10,688,106 SNPs and indels with allele frequency ≥ 5% in either this dataset, Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author one of the three batches of Simons Simplex Collection data, or GnomAD genomes, variants were pruned for independence with linkage disequilibrium r2 < 0.1 and then randomly downsampled to 118,849 variants. Principal component analysis was run using Hail 0.1. The first two principal components were used to classify samples by ancestry, and the first five principal components were used as covariates in the identification of eQTL loci (Figure S1).

Using the first two principal components of SNP-based ancestry estimated above, we identified clusters corresponding to African American ancestry (42 samples, 24% of cohort), European ancestry (82 sample, 47%), and Asian ancestry (4 samples, 2%), while 48 samples (27%) were outside of these clusters and enriched for individuals who identified as Hispanic, Alaskan native, or mixed ancestry (Figure S1; Table S1). Analyzing the first principal component of ancestry shows ancestry groups were differentially represented across developmental periods (F = 3.4, df = 5, p = 0.006, ANOVA), with post hoc analysis showing a greater representation of African American samples in later developmental periods compared with samples of Hispanic, Alaskan native, or mixed ancestry (p.adj = 0.02, TukeyHSD).

Estimation of biological and technical covariates in RNA-seq data—PCA was performed on the covariance matrix of 23,782 cortically expressed genes in 176 samples. A secondary PCA was performed on the 11,705 Non-transitional genes in 167 samples (excluding Period 1 and 2 samples) to assess the extent to which removing the late-fetal transition accounted for variance in gene expression. For each PCA, the variance explained by each principal component was assessed (Figure S2).

To quantify the relative contributions of biological and technical covariates, we calculated the partial R2 of each covariate with each principal component using the rsq R package, in a generalized linear model where loadings of principal components are considered as a response and biological and technical covariates (such as developmental period, sex, sequencing batch, sequencing depth, RNA integrity number (RIN), mitochondrial RNA proportion, ribosomal RNA proportion, intronic reads proportion, intergenic reads proportion) are considered as predictor variables (Figure S2).

Comparison between BrainVar and BrainSpan—While the BrainVar bulk tissue RNA-seq dataset catalogs gene expression in the DLPFC of 176 donors across development (6 post-conception weeks to 20 years of age), BrainSpan catalogs gene expression across 16 brain regions, including DLPFC, in 40 brains ranging from 8 post-conception weeks to 40 postnatal years (Li et al., 2018). Of note, data were generated for 14 brains in both BrainVar and BrainSpan. To compare the data from the 14 samples profiled in both BrainSpan (Li et al., 2018) and BrainVar, we reprocessed the entire BrainSpan dataset using the BrainVar RNA-seq analysis pipeline, transforming raw read counts to log2CPM values. We then filtered to genes that passed BrainVar minimum expression criteria and calculated sample- to-sample and geneto-gene Pearson correlation coefficients. We performed principal component analysis on a covariance matrix of the 14 commonly sequenced samples and the 23,782 BrainVar-expressed genes for both datasets. Despite differences in library

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preparation, with BrainSpan using poly-A priming compared to TruSeq random priming in Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author BrainVar, gene expression was highly correlated per-sample and per-gene.

BrainSpan also generated ChIP-seq datasets of histone 3 lysine 27 acetylation (H3K27ac), a marker of active genes, in DLPFC samples (Li et al., 2018). These H3K27ac data mirror the expression profiles in BrainVar, with Falling genes enriched for fetal-biased H3K27ac peaks and depleted for adult-biased H3K27ac peaks, while Rising genes show the opposite pattern of enrichment (Figure S2).

Transcriptome temporal trajectory estimation—Statistically, the temporal dynamics of the expression of genes can be modeled as a mixture of K distinct trajectories, each with Gaussian noise (Jones et al., 2001; Roeder et al., 1999). To delineate the temporal dynamics for K groups of genes, we used the Flexmix R package, which provides the expected trajectory for each group and the soft group assignments of individual genes to groups.

The expression of 23,782 genes was transformed as log2(CPM) and normalized by the interquartile range. The samples in epoch 0 were excluded to avoid biased estimation due to very few samples. To identify the overall trend of expression over age, first we fitted the model on all the 23,782 genes assuming there are three groups and that the expected trajectories for each group can be represented with degree-4 polynomials on age. Three typical trajectories were identified, including a group of 6,941 genes with Rising expression levels, a group of 5,173 with Falling expression levels, and a group of 11,705 genes with roughly flat (fitted) expression over time, which we called Non-transitional (Figure 2B).

We considered whether fewer or more trajectory groups described the data better. If we input more than four groups, adjacent classes are automatically combined due to the estimated priors falling below the minimum threshold leaving a maximum of four groups. Using the Akaike information criterion (AIC) and Bayesian information criterion (BIC) we observe the lowest values, indicating a better fit to the data, for four groups, followed closely by three groups, but not for two groups. However, the additional group does not reveal a new profile (e.g., genes rising to period 8/9 then falling for 10–12), instead it distinguishes two Rising groups based on gene variance. Since our objective was to identify temporal gene trajectories, not differences in variance, we selected three groups for the final model to avoid overfitting and ensure interpretability.

Gene ontology functional enrichment for temporal trajectories—For functional enrichment, we characterized genes sets for each trajectory using the R package, gProfiler (Reimand et al., 2011). The pathway enrichment test was performed using Gene Ontology Biological Process terms, which contain between 10 and 2,000 genes, and all 23,782 cortically expressed genes were used as background. Enrichment tests were subject to the “moderate” hierarchical filtering parameter, and the FDR multiple correction in the gProfiler (Figure 2F).

Assessing enrichment in tissue-specific genes from GTEx—To assess enrichment across tissues, 27,546 transcripts with an RPKM ≥ 0.5 in 80% of samples one or more tissues in GTEx (https://gtexportal.org/home/) were defined and log-transformed

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(log2[RPKM+1]). For each gene, expression between each tissue and all other tissues was Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author assessed using a moderated t-test (R package limma), with models adjusted for age, RIN, gender, and surrogate variables. The Benjamini and Hochberg method was used to estimate false discovery rate (FDR) and tissue enriched genes were defined as: log fold-change > 0.5 and FDR < 0.05. The enrichment of the Falling, Non-transitional, and Rising genes was assessed with using Fisher’s exact test with 23,782 cortically expressed genes as a background (Figure 2).

Identifying genes enriched in cell types from single cell data—To identify cell type-enriched genes we calculated a tau metric (Kryuchkova-Mostacci and Robinson- Rechavi, 2017) from the per gene log2TPM of log2UMI values for genes within cell type clusters in prenatal forebrain (Nowakowski et al., 2017) and postnatal cortex (Li et al., 2018). Each gene was also ranked across all clusters within the dataset on the basis of TPM/ UMI, so that the cluster with the highest TPM/UMI was ranked as “1,” while the cluster with the second highest TPM/UMI for that gene was ranked as “2,” etc. The genes were sorted by TPM/UMI rank and visually inspected for cell type specificity or substantial enrichment in single cell RNA-seq data from the prenatal and postnatal human cortex (https://cells.ucsc.edu/?ds=cortex-dev#). For each cell type, the top ten genes that showed clear specificity/enrichment were selected.

Enrichment of gene trajectories in temporal putative cis-regulatory elements —H3K27ac peaks present in more than two samples of fetal or adult dorsal frontal cortex in BrainSpan (Li et al., 2018) were tested for fetal versus adult temporal bias using DESeq2 (Love et al., 2014). Temporally biased genes were defined as adjusted p < 0.01 and fold change ≥ 2. A category of “non-temporal” H3K27ac peaks was generated with peaks showing p > 0.05. All peaks were annotated using the gene with the closest transcription start site in Gencode v21. Genes were classified as only-fetal or only-adult if they were associated with fetal or adult-only H3K27ac peaks, respectively. Enrichments of each category of H3K27ac-genes in each category of eGenes were tested by means of a Fisher Exact’s test and p-values were adjusted using Benjamini-Hochberg, using genes associated to non-variant H3K27ac peaks as a reference background.

WGCNA network construction and module definition—To assess the functional topology in cortical samples, we applied Weighted Gene Co-Expression Network Analysis (WGCNA) (Langfelder and Horvath, 2008) to 23,782 cortically expressed transcripts. Network analysis was performed with WGCNA (version 1.63) using a signed network, choosing a soft-threshold power, the mean connectivity less than 50, and scale-free topology greater than 0.8. To reduce the bias driven by a few sample outliers, we applied the blockwiseConsensusModules function, which detects consensus modules across 100 subsampled networks. We used the average linkage hierarchical clustering of the topological overlap dissimilarity matrix (1-TOM) to generate the network dendrogram. Modules were defined as branches of the dendrogram using the hybrid adaptive tree cut with the following parameters: minimum module size = 200, negative pamStage, height cut = 0.999, and deep split = 2 (Langfelder and Horvath, 2007). Modules were summarized by their first principal

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component (ME, module eigengene), followed by merging modules with high correlations Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author (eigengene value ≥ 0.9).

WGCNA functional enrichment for module characterization—For functional enrichment, we characterized WGCNA module genes using the gProfiler R package (Reimand et al., 2011), as described above in the analysis of temporal trajectory genes. To identify WGCNA module genes that are regulatory targets, we searched for transcription factor binding targets using the ChEA (Lachmann et al., 2010; Satterstrom et al., 2020), and TRANSFAC (Matys et al., 2003), and microRNA using the mirTarbase database (Chou et al., 2018).

WGCNA module preservation—To assess whether 19 co-expression modules in our samples were preserved in other, independent DLPFC or frontal cortex expression datasets, we compared our dataset with non-overlapping samples from the BrainSpan dataset (Li et al., 2018) and applied the module preservation function from the WGCNA R package (Langfelder et al., 2011). From the BrainSpan dataset, we selected DLPFC (n = 30), 10 non- DLPFC neocortical regions (n = 317), and subcortical regions excluding the cerebellum (n = 140). Given the original co-expression network constructed above, we used modulePreservation to calculate module preservation statistics from 100 permutations (Table S3).

Clustering analysis in protein-protein interaction network—To examine functional association of a group of genes/proteins, we performed a clustering analysis of a protein- protein interaction network implemented in SANTA R package (Cornish and Markowetz, 2014). Ripley’s K function provides a measure of whether points are clustered together or randomly dispersed (homogeneous) in a network and the SANTA R package reformulated Ripley’s K function for a protein-protein interaction network. Clustering can be indicative of functional association between the genes/proteins under consideration. To test departure from homogeneity of a given gene set, we drew an empirical null distribution of clustering from 1,000 random samples of matching sized gene sets from the BioGRID protein-protein interaction network data (Stark et al., 2006; Winter et al., 2011) (v3.4.132). We reported departure from null distribution as a Z-score (i.e., Z > 0: level of clustering of a gene set greater than null expectation and Z < 0: level of clustering of a gene set less than null expectation).

Cis-eQTL detection and classification—Cis-eQTLs were identified for all high quality, common variants (N = 6,573,196) within 1 Mb of a gene boundary using the linreg function in Hail 0.1, with period, sex, and the first five principal components of common variant ancestry as covariates. This analysis was run on three cuts of the BrainVar dataset: complete sample (N = 176, periods 1–12), prenatal-only (N = 112, periods 1–6), and postnatal-only (N = 60, periods 8–12). Separately for the results of each analysis, false discovery rate (FDR) was calculated for all gene-variant pairs using the Benjamini- Hochberg procedure.

We then classified all gene-variant pairs with FDR ≤ 0.05 from at least one analysis into groups defined by the temporal specificity of their eQTL effects. To do this, we first

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identified one variant per gene with the smallest, FDR-significant p value, from any of the Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author three analyses. We then used a Z-test to compare the regression coefficients for these variant-gene pairs from the prenatal and postnatal analyses:

βPre − βPost Z = 2 2 SEPre + SEPost

Using the results from the eQTL analyses and from this prenatal-postnatal comparison, we then classified each of these top (one per gene) gene-variant pairs and their corresponding target gene (eGene) into one of five groups:

1. Constant eQTLs/eGenes, characterized by consistent effects across this developmental dataset: FDR ≤ 0.05 in the complete sample analysis, same direction of effect and unadjusted p ≤ 0.05 in both the prenatal and postnatal analyses

2. Prenatal-predominant eQTLs/eGenes, with strongest effects during prenatal development: FDR ≤ 0.05 in the prenatal analysis, unadjusted p > 0.05 in the postnatal analysis, pre-post comparison Z-test FDR-adjusted p ≤ 0.05

3. Postnatal-predominant eQTLs/eGenes, with strongest effects during postnatal development: FDR ≤ 0.05 in the postnatal analysis, unadjusted p > 0.05 in the prenatal analysis, pre-post comparison Z-test FDR-adjusted p ≤ 0.05

4. Prenatal-trending eQTLs/eGenes, which did not fit into earlier categories, but had higher prenatal effects (BPre > BPost) 5. Postnatal-trending eQTLs/eGenes, which did not fit into earlier categories, but had higher prenatal effects (BPost > BPre) All FDR-significant variants associated with the expression of a single gene were classified into one of these five groups according to the classification of the top variant for the same gene (Figure 5).

Alternative approaches for assigning eGenes to temporal categories—Many eGenes are associated with multiple eQTLs, each of which could individually meet criteria for any one of the five temporal categories. As described above, we categorized eGenes into temporal categories based on the performance of their top eQTL (smallest p-value), but we assessed the performance against three alternative approaches: (1) eGene assigned to the same category as a majority of their eQTLs (“majority eQTL” approach), with ties assigned in the order Constant, Prenatal-predominant, Postnatal-predominant, Prenatal-trending, Postnatal-trending, (2) for eGenes with ≥ 1 eQTL, category assignment based on the performance of the second most significant variant (“second eQTL” approach), and (3) each eQTL individually assigned to categories (“individual eQTL” approach). For each of these alternative approaches, we calculated the percent of eGenes or eQTLs from each top variant- based category that were assigned to each category using the majority eQTL, the second eQTL, or the individual eQTL approach (Figure S5).

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Assessment of ancestry differences in prenatal and postnatal sample sets Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author using genomic control—To validate that our eQTL discovery analyses were adequately adjusted for differences in sample ancestry, we calculated genomic control, or lambda, an estimate of inflation of genetic association signal (Devlin and Roeder, 1999), from the results of each of the three analyses (complete sample, prenatal-only, postnatal-only). To do this, we randomly sampled 500 gene-variant pairs that occur on the same chromosome but are located 10–100 Mb apart, under the assumption that these distant gene-variant pairs would be unlikely to be enriched for true gene expression association. Using the eQTL signals for these 500 gene-variant pairs from the three analyses, we calculated lambda for analysis. We repeated this procedure for 100 random selections of 500 distant gene-variant pairs. We find that the median lambda values across 100 permutations for each are near 1, in keeping with a test has been properly corrected for population structure (specifically, 1.01 for prenatal, 0.99 for postnatal, and 1.00 for the complete sample).

Comparison with published eQTL studies—To assess the sensitivity of our cis-eQTL discovery analysis relative to previous work, we evaluated the relationship between sample size and eGene discovery for: the BrainVar prenatal, postnatal, and complete sample analyses as run using the HCP- and SVA-adjusted expression data, GTEx v7 analyses by tissue (gtexportal.org), postnatal human frontal cortex by the CommonMind Consortium (Fromer et al., 2016), and prenatal human whole brain (O’Brien et al., 2018). Using the sample size reported in each analysis, or for each tissue (GTEx), and the number of genes with at least one eQTL reaching significance of FDR ≤ 0.05, we plotted the relationship between eGene discovery and sample size (Figure S5). We observe a strongly positive relationship across BrainVar and the published analyses, in keeping with prior reports that eQTL and eGene discovery is positively associated with sample size (Aguet et al., 2017).

We evaluated the performance of the eQTLs that we identified in our analyses with published sets of eQTLs identified in the human postnatal frontal cortex (Aguet et al., 2017) and in the human prenatal whole brain (O’Brien et al., 2018). For the postnatal frontal cortex data, we downloaded significant variant-gene pairs from the GTEx v7 data release from gtexportal.org and used R to write out the variant locations to a bed file format. We then used the command line LiftOver utility from the UCSC Genome Browser to convert the hg19 variant positions to GRCh38. For the prenatal brain data, we downloaded the eQTL summary statistics, results for the top eQTLs per gene, and SNP positions bed file from the study data repository on Figshare (https://figshare.com/articles/ Summary_statistics_for_expression_quantitative_trait_loci_in_the_developing_human_brai n_and_their_enrichment_in_neuropsychiatric_disorders/6881825). We then used each gene’s nominal significance threshold from the top eQTLs file to identify the full set of variant-gene pairs meeting significance.

Using variant (GRCh38 position, reference, and alternate alleles) and gene (Ensembl gene IDs) identifiers, we matched significant variant-gene pairs separately from GTEx frontal cortex and prenatal brain to the variant-gene pairs meeting FDR significance (%0.05) in the BrainVar analyses. For all significant eQTLs in BrainVar that overlapped with the reference datasets, we then compared the effect of the variant on the expression of its associated gene to determine the percentage of overlapping eQTLs with concordant direction of effect, as

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well as the Pearson correlation between the eQTL effects (beta from BrainVar, slope from Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author GTEx or prenatal whole brain). The significance of this correlation was evaluated using the cor.test function in R.

Distance between eQTLs and transcription start site—The distance between each significant eQTL and the transcription start site (TSS) of its associated eGene was calculated by comparing the variant position to the TSS position and strand of the gene according to Gencode v21. Positive distances indicate variants downstream of the TSS, negative distances indicate upstream variants. Comparison between groups of eQTLs was run using only the top eQTL per gene and the absolute value of distance from the associated gene’s TSS.

Overlap of eQTLs with H3K27ac—We tested the global overlap between eQTLs and H3K27ac from human fetal, infant and adult dorsal frontal cortex and cerebellum and embryonic cortex from BrainSpan (Li et al., 2018) and Reilly et al. (2015). Intersection between sets of coordinates were performed using Bedtools (Quinlan and Hall, 2010). We tested three sets of variants: (1) best eQTL per eGENE, (2) all significant eQTLs with FDR < 0.05 in the corresponding eQTL category of Prenatal-predominant, Postnatal-predominant or Constant, and (3) a background group composed by all variants tested for eQTL, excluding those with a p < 0.05 to any gene at any time period tested. 95% confidence intervals were obtained by bootstrapping variants 100 times (Figure S5).

Enrichment of eQTLs in functional genomic elements—We tested the enrichment of different categories of eQTLs in sets of genomics elements using GREGOR (Schmidt et al., 2015). We tested all best eQTL per gene in: (1) dorsal frontal cortex H3K27ac peaks from fetal and adult brain samples, and (2) 18 chromatin states whole-genome segmentation of sample E073-Medial Frontal Cortex Lobe from The Roadmap Epigenomics Project (Kundaje et al., 2015). We reported observed/expected number of overlapping eQTLs and BH adjusted p-values (Figure S5).

Test for differential expression variance in prenatal and postnatal stages—We used an F test to compare prenatal variance (periods 1–6) and postnatal variance (periods 8– 12) in gene expression level, correcting for age (period), sex, and five ancestry principal components within each stage. At a Benjamini-Hochberg-adjusted p value ≤ 0.05, we identified 8,094 genes with greater prenatal variance, and 1,752 genes with greater postnatal variance.

Gene sets associated with CNS traits and disorders—To compare with 23,782 cortically expressed genes, we created the list of gene sets for previous trait and disorder association and functional properties. Gene identifiers were converted between studies based on the complete HUGO Gene Nomenclature Committee dataset. Autism spectrum disorder (ASD) risk genes were obtained from Satterstrom et al. (2020), an exome sequencing based gene discovery refining to high-confidence genes (n = 99) at a false discovery rate (FDR) ≤ 0.1. Genes associated with developmental delay (n = 93) were selected from the exome sequencing analysis of the Deciphering Developmental Disorders project (Deciphering Developmental Disorders Study, 2017). From Heyne et al. (2018), we chose 33 genes as high-confidence epilepsy candidates, where multiple de novo variants were seen.

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We used significant loci from the genome-wide association studies (GWAS) of attention Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author deficit hyperactivity disorder (ADHD) (Demontis et al., 2019), Alzheimer’s disease (Lambert et al., 2013), educational attainment (Lee et al., 2018), schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), major depressive disorder (Wray et al., 2018), multiple sclerosis (Beecham et al., 2013), Parkinson’s disease (Chang et al., 2017). We selected loci from a summary statistics file if publicly available, otherwise we used the table of genome-wide significant loci from each study. We retrieved genes harboring loci or within 10kb from loci. We excluded the extended major histocompatibility complex region on the chromosome 6, known to have a substantial number of genes due to high linkage disequilibrium (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014) for downstream enrichment analyses.

Constrained genes were defined as probability of loss-of-function intolerant (pLI) score ≥ 0.995 in the ExAC database (Lek et al., 2016). Genes specific to cell types in the mid-fetal cortical development were selected from Nowakowski et al. (2017) and BrainSpan (Li et al., 2018). For all gene lists, see Table S2 and Table S4.

Enrichment of DLPFC eQTLs in SNPs associated with complex phenotypes— We tested for enrichment of DLPFC eQTLs among GWAS significant SNPs using a permutation-based procedure. GWAS SNPs were taken from published GWAS summary statistics at a significance threshold of p < 5 × 10−8. The following procedure was repeated separately for summary statistics from GWAS of four phenotypes: schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), educational attainment (Lee et al., 2018), multiple sclerosis (Beecham et al., 2013), and Alzheimer’s disease (Lambert et al., 2013). First, SNPs in the summary statistics and the list of SNPs tested for eQTL discovery were filtered to those in 1000 Genomes Project data. SNPs in the GWAS summary statistics were then filtered to variants also tested for eQTL discovery, using direct (same SNP) or proxy (r2 > 0.8 in CEU 1000 Genomes samples) as defined by the PLINK 1.9 – r2 command (Chang et al., 2015a). To estimate the proportion of eQTL SNPs among phenotype associated SNPs versus among all SNPs (or a sample of null SNPs), we recognized three important factors that could differ between null SNP sets and the phenotype-associated SNPs: LD structure, MAF distribution, and gene density. First, to account for LD structure, we used PriorityPruner version 0.1.4 (http:// prioritypruner.sourceforge.net) to LD prune SNPs supervised by GWAS p-value in order to preferentially retain as many phenotype-associated SNPs as possible while adequately removing SNPs in high LD (r2 > 0.7 within a sliding 500kb window) using in 1000 Genomes CEU sample data. Second, the remaining SNPs were grouped according to their MAF decile. Third, each remaining SNP was grouped into decile of gene density to allow for differential opportunity to be identified as an eQTL. Gene density was determined by the number of genes within the 1MB eQTL detection window as defined by the annotation package “TxDb.Hsapiens.UCSC.hg38.knownGene” from R Bioconductor (Huber et al., 2015). SNPs in the ~3.2 MB HLA region (hg38 coordinates: chr6:29,751,784–32,915,731) as defined by the “GWASTools” R Bioconductor package (Gogarten et al., 2012) and UCSC genome browser (Kent et al., 2002) were excluded from enrichment testing. Next, 1 million null SNP sets were drawn by matching each phenotype-associated SNP (GWAS p < 5 ×

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10−8) to a random SNP matched on both MAF and gene density. Enrichment fold statistics Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author were computed as the proportion of eQTLs in the phenotype-associated set divided by the mean proportion of eQTLs across null sets. P values were calculated as the proportion of null set fold-enrichment statistics as or more extreme than the observed phenotype- associated fold enrichment statistic. This permutation procedure was repeated for each of six eQTL SNP lists: all, Constant, Prenatal-predominant, Prenatal-trending, Postnatal- predominant, and Postnatal-trending.

Gene-set analysis of eGenes and GWAS data—To assess whether eQTL targets (eGenes) are enriched for GWAS signal, we performed competitive gene set enrichment analysis for each group of eGenes using the MAGMA software (de Leeuw et al., 2015). We input the eGene lists from Prenatal-predominant, Postnatal-predominant, Constant, Prenatal- trending, and Postnatal-trending (Table S5) and summary statistics from published GWAS of schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), autism spectrum disorder (Grove et al., 2019), educational attainment (Lee et al., 2018), multiple sclerosis (Beecham et al., 2013), Alzheimers disease (Lambert et al., 2013), triglycerides (Willer et al., 2013), and height (Wood et al., 2014). First, SNPs from the GWAS summary statistics files were annotated to NCBI protein-coding genes that passed RNA-seq QC in our DLPFC expression data with a 10kb window on either side of the gene boundaries. Next, a gene-level analysis was done to determine the strength of association for each gene with phenotype of interest. To assess whether genes in the eGene gene-sets are more strongly associated with the phenotype of interest than other genes, gene-based z- scores are regressed on a gene-set indicator variable and MAGMA default covariates (gene size, gene density, sample size, 1/MAC, and the log of each of these). The beta coefficient for the gene-set indicator variable is tested for significance HA : β1 > 0. Results from this analysis are reported in Table S6. We did not observe significant enrichment of GWAS signal from any of the six phenotypes tested in any of the five temporally assigned eGene gene sets. However, we repeated a similar test, annotating GWAS summary statistics SNPs to NCBI protein-coding genes with 10kb flanking region that passed RNA-seq QC in our DLPFC expression data and had an assigned pLI score, and found that a gene-set defined as pLI score > = 0.995 showed significant enrichment for stronger GWAS association in all tested phenotypes except for multiple sclerosis, compared to genes with pLI score < 0.995.

Co-localization analysis of CNS traits and disorders—Coloc (Giambartolomei et al., 2014) was used to formally test for co-localization of GWAS signal from schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014) and educational attainment (Lee et al., 2018) summary statistics with our five eQTL categories. Coloc tests five hypotheses (H0: no association, H1: GWAS association only, H2: eQTL association only, H3: both but not co-localized, H4: both and co-localized) and returns a posterior probability for each hypothesis in each region. Posterior probably of H4 > = 0.8 is strong Bayesian evidence of co-localization.

DATA AND CODE AVAILABILITY Open source scripts used in this study are referenced throughout. The pipeline for whole- genome analysis is available online at: https://github.com/sanderslab/psychcore-compute-

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platform. The accession number for the raw RNA-seq and WGS data reported in this paper, Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author along with processed files, is PsychENCODE Knowledge Portal: syn21557948 on Synapse.org (https://www.synapse.org/#!Synapse:syn4921369).

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

ACKNOWLEDGMENTS

Data were generated as part of the PsychENCODE Consortium, supported by U01MH103339, U01MH103365, U01MH103392, U01MH103340, U01MH103346, R01MH105472, R01MH094714, R01MH105898, R21MH102791, R21MH105881, R21MH103877, and P50MH106934 awarded to Schahram Akbarian (Icahn School of Medicine at Mount Sinai), Gregory Crawford (Duke), Stella Dracheva (Icahn School of Medicine at Mount Sinai), Peggy Farnham (USC), Mark Gerstein (Yale), Daniel Geschwind (UCLA), Thomas M. Hyde (LIBD), Andrew Jaffe (LIBD), James A. Knowles (USC), Chunyu Liu (UIC), Dalila Pinto (Icahn School of Medicine at Mount Sinai), Nenad Sestan (Yale), Pamela Sklar (Icahn School of Medicine at Mount Sinai), Matthew State (UCSF), Patrick Sullivan (UNC), Flora Vaccarino (Yale), Sherman Weissman (Yale), Kevin White (UChicago), and Peter Zandi (JHU). This work was supported by funding provided by Autism Science Foundation postdoctoral fellowships (to D.W. and J.-Y.A.) and a research award (to S.J.S.); Simons Foundation Autism Research Initiative (SFARI) grants 574598 (to S.J.S.), 402281 (to S.J.S., M.W.S., B.D., and K.R.), and 573206 (to M.E.T.); National Institute for Mental Health (NIMH) grants R01 MH109901 (to S.J.S. and M.W.S.), R01 MH110928 (to S.J.S. and M.W.S.), U01 MH103339 (to M.W.S.), R01 MH111662 (to S.J.S. and M.W.S.), U01 MH105575 (to M.W.S.), U01 MH106874 (to N.S.), P50 MH106934 (to N.S.), R01 MH109904 (to N.S.), R01 MH110926 (to N.S.), U01 MH116488 (to N.S.), R37 MH057881 (to B.D.), and R01 MH115957 (to M.E.T.); National Institute of Child Health and Human Development (NICHD) grants R24 HD000836 (to I.A.G.), R01 HD081256 (to M.E.T.), and R01 HD096326 (to M.E.T.); National Heart, Lung, and Blood Institute (NHLBI) grant K25HL121295 (to N.A.Z.); National Human Genome Research Institute (NHGRI) grants U01HG009080 (to N.A.Z.) and R01HG006399 (to N.A.Z.); National Cancer Institute (NCI) grant R01CA227237 (to N.A.Z.); National Institute of Dental and Craniofacial Research (NIDCR) grant R03DE025665 (to N.A.Z.); United States Department of Defense (DoD) grant W81XWH-16-2-0018 (to N.A.Z.); and National Research Foundation of Korea grant 2019M3E5D3073568 (to J.-Y.A.). The project that gave rise to these results also received the support of a fellowship from “la Caixa” Foundation (ID 100010434 to G.S.). The fellowship code is LCF/BQ/PI19/11690010. We thank Thomas Lehner, Anjené Addington, Geetha Senthil, and Alexander Arguello at the NIMH for supporting the PsychENCODE Consortium, the Yale Center for Genome Analysis for generating the RNA-seq data, GENEWIZ for generating the WGS data, and Sentieon for use of a computationally efficient implementation of GATK Haplotype Caller.

REFERENCES Aguet F, Brown AA, Castel SE, Davis JR, He Y, Jo B, Mohammadi P, Park Y, Parsana P, Segrè AV, et al.; GTEx Consortium (2017). Genetic effects on gene expression across human tissues. Nature 550, 204–213. 10.1038/nature24277. [PubMed: 29022597] Akbarian S, Liu C, Knowles JA, Vaccarino FM, Farnham PJ, Crawford GE, Jaffe AE, Pinto D, Dracheva S, Geschwind DH, et al.; PsychENCODE Consortium (2015). The PsychENCODE project. Nat. Neurosci. 18, 1707–1712. [PubMed: 26605881] An JY, Lin K, Zhu L, Werling DM, Dong S, Brand H, Wang HZ, Zhao X, Schwartz GB, Collins RL, et al. (2018). Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 362, eaat6576. [PubMed: 30545852] Anders S, Pyl PT, and Huber W (2015). HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169. [PubMed: 25260700] Bae BI, and Walsh CA (2013). Neuroscience. What are mini-brains? Science 342, 200–201. [PubMed: 24115427] Beecham AH, Patsopoulos NA, Xifara DK, Davis MF, Kemppinen A, Cotsapas C, Shah TS, Spencer C, Booth D, Goris A, et al.; International Multiple Sclerosis Genetics Consortium (IMSGC); Wellcome Trust Case Control Consortium 2 (WTCCC2); International IBD Genetics Consortium (IIBDGC) (2013). Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet. 45, 1353–1360. [PubMed: 24076602]

Cell Rep. Author manuscript; available in PMC 2020 June 15. Werling et al. Page 29

Birnbaum R, and Weinberger DR (2017). Genetic insights into the neurodevelopmental origins of Author ManuscriptAuthor Manuscript Author schizophrenia. Manuscript Author Nat. Rev. Manuscript Author Neurosci. 18, 727–740. [PubMed: 29070826] Bonneau A, and Parmar N (2012). Effects of RhebL1 silencing on the mTOR pathway. Mol. Biol. Rep. 39, 2129–2137. [PubMed: 21655954] BrainSeq: A Human Brain Genomics Consortium (2015). BrainSeq: Neurogenomics to Drive Novel Target Discovery for Neuropsychiatric Disorders. Neuron 88 (6), 1078–1083. 10.1016/ j.neuron.2015.10.047. [PubMed: 26687217] Breen G, Li Q, Roth BL, O’Donnell P, Didriksen M, Dolmetsch R, O’Reilly PF, Gaspar HA, Manji H, Huebel C, et al. (2016). Translating genome-wide association findings into new therapeutics for psychiatry. Nat. Neurosci. 19, 1392–1396. [PubMed: 27786187] Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, and Lee JJ (2015a). Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7. [PubMed: 25722852] Chang J, Gilman SR, Chiang AH, Sanders SJ, and Vitkup D (2015b). Genotype to phenotype relationships in autism spectrum disorders. Nat. Neurosci. 18, 191–198. [PubMed: 25531569] Chang D, Nalls MA, Hallgrímsdóttir IB, G.A., Ayalon G, Bingol B, Sheng M, et al.; International Parkinson’s Disease Genomics Consortium; 23andMe Research Team (2017). A meta-analysis of genome-wide association studies identifies 17 new Parkinson’s disease risk loci. Nat. Genet. 49, 1511–1516. [PubMed: 28892059] Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, et al. (2018). miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 46 (D1), D296–D302. [PubMed: 29126174] Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, Colantuoni EA, Elkahloun AG, Herman MM, Weinberger DR, and Kleinman JE (2011). Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523. [PubMed: 22031444] Cornish AJ, and Markowetz F (2014). SANTA: quantifying the functional content of molecular networks. PLoS Comput. Biol. 10, e1003808. [PubMed: 25210953] Courchesne E, Pierce K, Schumann CM, Redcay E, Buckwalter JA, Kennedy DP, and Morgan J (2007). Mapping early brain development in autism. Neuron 56, 399–413. [PubMed: 17964254] de Leeuw CA, Mooij JM, Heskes T, and Posthuma D (2015). MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219. [PubMed: 25885710] Deciphering Developmental Disorders Study (2017). Prevalence and architecture of de novo mutations in developmental disorders. Nature 542, 433–438. [PubMed: 28135719] Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, Baldursson G, Belliveau R, Bybjerg-Grauholm J, Bækvad-Hansen M, et al.; ADHD Working Group of the Psychiatric Genomics Consortium (PGC); Early Lifecourse & Genetic Epidemiology (EAGLE) Consortium; 23andMe Research Team (2019). Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75. [PubMed: 30478444] Devlin B, and Roeder K (1999). Genomic control for association studies. Biometrics 55, 997–1004. [PubMed: 11315092] Dobbyn A, Huckins LM, Boocock J, Sloofman LG, Glicksberg BS, Giambartolomei C, Hoffman GE, Perumal TM, Girdhar K, Jiang Y, et al.; CommonMind Consortium (2018). Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS. Am. J. Hum. Genet. 102, 1169–1184. [PubMed: 29805045] Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, and Gingeras TR (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21. [PubMed: 23104886] Domazet-Loso T, and Tautz D (2010). A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns. Nature 468, 815–818. [PubMed: 21150997] Ecker C, Bookheimer SY, and Murphy DG (2015). Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan. Lancet Neurol. 14, 1121–1134. [PubMed: 25891007] Elliott LT, Sharp K, Alfaro-Almagro F, Shi S, Miller KL, Douaud G, Marchini J, and Smith SM (2018). Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210–216. [PubMed: 30305740]

Cell Rep. Author manuscript; available in PMC 2020 June 15. Werling et al. Page 30

Freed DN, Aldana R, Weber JA, and Edwards JS (2017). The Sentieon Genomics Tools - A fast and Author ManuscriptAuthor Manuscript Author Manuscriptaccurate Author solution to Manuscript Author variant calling from next-generation sequence data. bioRxiv. 10.1101/115717. Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, Ruderfer DM, Oh EC, Topol A, Shah HR, et al. (2016). Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453. [PubMed: 27668389] Geschwind DH, and Flint J (2015). Genetics and genomics of psychiatric disease. Science 349, 1489– 1494. [PubMed: 26404826] Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, and Plagnol V (2014). Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383. [PubMed: 24830394] Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, Arepalli S, Dillman A, Rafferty IP, Troncoso J, et al. (2010). Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 6, e1000952. [PubMed: 20485568] Gogarten SM, Bhangale T, Conomos MP, Laurie CA, McHugh CP, Painter I, Zheng X, Crosslin DR, Levine D, Lumley T, et al. (2012). GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies. Bioinformatics 28, 3329–3331. [PubMed: 23052040] Grove J, Ripke S, Als TD, Mattheisen M, Walters R, Won H, Pallesen J, Agerbo E, Andreassen OA, Anney R, et al. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444. [PubMed: 30804558] GTEx Consortium (2015). Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660. [PubMed: 25954001] Gulsuner S, Walsh T, Watts AC, Lee MK, Thornton AM, Casadei S, Rippey C, Shahin H, Nimgaonkar VL, Go RC, et al.; Consortium on the Genetics of Schizophrenia (COGS); PAARTNERS Study Group (2013). Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell 154, 518–529. [PubMed: 23911319] Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, et al. (2012). GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774. [PubMed: 22955987] Heinzen EL, Ge D, Cronin KD, Maia JM, Shianna KV, Gabriel WN, Welsh-Bohmer KA, Hulette CM, Denny TN, and Goldstein DB (2008). Tissue-specific genetic control of splicing: implications for the study of complex traits. PLoS Biol. 6, e1. Heyne HO, Singh T, Stamberger H, Abou Jamra R, Caglayan H, Craiu D, De Jonghe P, Guerrini R, Helbig KL, Koeleman BPC, et al.; EuroEPINOMICS RES Consortium (2018). De novo variants in neurodevelopmental disorders with epilepsy. Nat. Genet. 50, 1048–1053. [PubMed: 29942082] Hu WF, Chahrour MH, and Walsh CA (2014). The diverse genetic landscape of neurodevelopmental disorders. Annu. Rev. Genomics Hum. Genet. 15, 195–213. [PubMed: 25184530] Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, et al. (2015). Orchestrating high-throughput genomic analysis with Bioconductor. Mat. Methods 12, 115–121. Insel TR (2010). Rethinking schizophrenia. Nature 468, 187–193. [PubMed: 21068826] Jaffe AE, Straub RE, Shin JH, Tao R, Gao Y, Collado-Torres L, KamThong T, Xi HS, Quan J, Chen Q, et al.; BrainSeq Consortium (2018). Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat. Neurosci. 21, 1117–1125. [PubMed: 30050107] Jones BL, Nagin DS, and Roeder K (2001). A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories. Sociol. Methods Res. 29, 374–393. Jun G, Flickinger M, Hetrick KN, Romm JM, Doheny KF, Abecasis GR, Boehnke M, and Kang HM (2012). Detecting and estimating contamination of human DNA samples in sequencing and array- based genotype data. Am. J. Hum. Genet. 91, 839–848. [PubMed: 23103226] Kalinka AT, Varga KM, Gerrard DT, Preibisch S, Corcoran DL, Jarrells J, Ohler U, Bergman CM, and Tomancak P (2010). Gene expression divergence recapitulates the developmental hourglass model. Nature 468, 811–814. [PubMed: 21150996]

Cell Rep. Author manuscript; available in PMC 2020 June 15. Werling et al. Page 31

Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, Sousa AM, Pletikos M, Meyer KA, Sedmak G, Author ManuscriptAuthor Manuscript Author Manuscriptet Author al. (2011). Spatio-temporal Manuscript Author transcriptome of the human brain. Nature 478, 483–489. [PubMed: 22031440] Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, and Haussler D (2002). The human genome browser at UCSC. Genome Res. 12, 996–1006. [PubMed: 12045153] Keshavan MS, Giedd J, Lau JY, Lewis DA, and Paus T (2014). Changes in the adolescent brain and the pathophysiology of psychotic disorders. Lancet Psychiatry 1, 549–558. [PubMed: 26361314] Kosmicki JA, Churchhouse CL, Rivas MA, and Neale BM (2016). Discovery of rare variants for complex phenotypes. Hum. Genet. 135, 625–634. [PubMed: 27221085] Kryuchkova-Mostacci N, and Robinson-Rechavi M (2017). A benchmark of gene expression tissue- specificity metrics. Brief. Bioinform. 18, 205–214. [PubMed: 26891983] Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ, et al.; Roadmap Epigenomics Consortium (2015). Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330. [PubMed: 25693563] Lachmann A, Xu H, Krishnan J, Berger SI, Mazloom AR, and Ma’ayan A (2010). ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438–2444. [PubMed: 20709693] Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, et al.; European Alzheimer’s Disease Initiative (EADI); Genetic and Environmental Risk in Alzheimer’s Disease; Alzheimer’s Disease Genetic Consortium; Cohorts for Heart and Aging Research in Genomic Epidemiology (2013). Metaanalysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452– 1458. [PubMed: 24162737] Langfelder P, and Horvath S (2007). Eigengene networks for studying the relationships between co- expression modules. BMC Syst. Biol. 1, 54. [PubMed: 18031580] Langfelder P, and Horvath S (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559. [PubMed: 19114008] Langfelder P, Luo R, Oldham MC, and Horvath S (2011). Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057. [PubMed: 21283776] Law CW, Chen Y, Shi W, and Smyth GK (2014). voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29. [PubMed: 24485249] Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, NguyenViet TA, Bowers P, Sidorenko J, Karlsson Linnér R, et al.; 23andMe Research Team; COGENT (Cognitive Genomics Consortium); Social Science Genetic Association Consortium (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121. [PubMed: 30038396] Leek JT, Johnson WE, Parker HS, Jaffe AE, and Storey JD (2012). The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883. [PubMed: 22257669] Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O’Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB, et al.; Exome Aggregation Consortium (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291. [PubMed: 27535533] Li H (2014). Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics 30, 2843–2851. [PubMed: 24974202] Li H, and Durbin R (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. [PubMed: 19451168] Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, Sunkin SM, Li Z, Shin Y, Zhu Y, et al.; BrainSpan Consortium; PsychENCODE Consortium; PsychENCODE Developmental Subgroup (2018). Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615. [PubMed: 30545854] Lipton JO, and Sahin M (2014). The neurology of mTOR. Neuron 84, 275–291. [PubMed: 25374355] Liu C, Cheng L, Badner JA, Zhang D, Craig DW, Redman M, and Gershon ES (2010). Whole-genome association mapping of gene expression in the human prefrontal cortex. Mol. Psychiatry 15, 779– 784. [PubMed: 20351726]

Cell Rep. Author manuscript; available in PMC 2020 June 15. Werling et al. Page 32

Love MI, Huber W, and Anders S (2014). Moderated estimation of fold change and dispersion for Author ManuscriptAuthor Manuscript Author ManuscriptRNA-seq Author data with DESeq2. Manuscript Author Genome Biol. 15, 550. [PubMed: 25516281] Lui JH, Hansen DV, and Kriegstein AR (2011). Development and evolution of the human neocortex. Cell 146, 18–36. [PubMed: 21729779] Matys V, Fricke E, Geffers R, Gö ssling E, Haubrock M, Hehl R, Hornischer K, Karas D, Kel AE, Kel- Margoulis OV, et al. (2003). TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 31, 374–378. [PubMed: 12520026] McCarroll SA, and Hyman SE (2013). Progress in the genetics of polygenic brain disorders: significant new challenges for neurobiology. Neuron 80, 578–587. [PubMed: 24183011] McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, and DePristo MA (2010). The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303. [PubMed: 20644199] McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, and Cunningham F (2016). The Ensembl Variant Effect Predictor. Genome Biol. 17, 122. [PubMed: 27268795] Mostafavi S, Battle A, Zhu X, Urban AE, Levinson D, Montgomery SB, and Koller D (2013). Normalizing RNA-sequencing data by modeling hidden covariates with prior knowledge. PLoS ONE 8, e68141. [PubMed: 23874524] Myers AJ, Gibbs JR, Webster JA, Rohrer K, Zhao A, Marlowe L, Kaleem M, Leung D, Bryden L, Nath P, et al. (2007). A survey of genetic human cortical gene expression. Nat. Genet. 39, 1494– 1499. [PubMed: 17982457] Network and Pathway Analysis Subgroup of the Psychiatric Genomics Consortium (2015). Corrigendum: Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 926. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, and Cox NJ (2010). Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888. [PubMed: 20369019] Nowakowski TJ, Bhaduri A, Pollen AA, Alvarado B, Mostajo-Radji MA, Di Lullo E, Haeussler M, Sandoval-Espinosa C, Liu SJ, Velmeshev D, et al. (2017). Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323. [PubMed: 29217575] O’Brien HE, Hannon E, Hill MJ, Toste CC, Robertson MJ, Morgan JE, McLaughlin G, Lewis CM, Schalkwyk LC, Hall LS, et al. (2018). Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders. Genome Biol. 19, 194. [PubMed: 30419947] Parikshak NN, Luo R, Zhang A, Won H, Lowe JK, Chandran V, Horvath S, and Geschwind DH (2013). Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021. [PubMed: 24267887] Pedersen BS, and Quinlan AR (2017). Who’s Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy. Am. J. Hum. Genet. 100, 406–413. [PubMed: 28190455] Pletikos M, Sousa AM, Sedmak G, Meyer KA, Zhu Y, Cheng F, Li M, Kawasawa YI, and Sestan N (2014). Temporal specification and bilaterality of human neocortical topographic gene expression. Neuron 81, 321–332. [PubMed: 24373884] Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, Boehnke M, Abecasis GR, and Willer CJ (2010). LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337. [PubMed: 20634204] Quinlan AR, and Hall IM (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842. [PubMed: 20110278] Regier AA, Farjoun Y, Larson DE, Krasheninina O, Kang HM, Howrigan DP, Chen BJ, Kher M, Banks E, Ames DC, et al. (2018). Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nat. Commun. 9, 4038. [PubMed: 30279509]

Cell Rep. Author manuscript; available in PMC 2020 June 15. Werling et al. Page 33

Reilly SK, Yin J, Ayoub AE, Emera D, Leng J, Cotney J, Sarro R, Rakic P, and Noonan JP (2015). Author ManuscriptAuthor Manuscript Author ManuscriptEvolutionary Author genomics. Manuscript Author Evolutionary changes in promoter and enhancer activity during human corticogenesis. Science 347, 1155–1159. [PubMed: 25745175] Reimand J, Arak T, and Vilo J (2011). g:Profiler–a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res. 39, W307–15. [PubMed: 21646343] Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, and Smyth GK (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47. [PubMed: 25605792] Roeder K, Lynch KG, and Nagin DS (1999). Modeling Uncertainty in Latent Class Membership: A Case Study in Criminology. J. Am. Stat. Assoc. 94, 766–776. Rosti RO, Sadek AA, Vaux KK, and Gleeson JG (2014). The genetic landscape of autism spectrum disorders. Dev. Med. Child Neurol. 56, 12–18. [PubMed: 24116704] Sanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE, Cicek AE, Murtha MT, Bal VH, Bishop SL, Dong S, et al.; Autism Sequencing Consortium (2015). Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron 87, 1215–1233. [PubMed: 26402605] Sanders SJ, Neale BM, Huang H, Werling DM, An JY, Dong S, Abecasis G, Arguello PA, Blangero J, Boehnke M, et al.; Whole Genome Sequencing for Psychiatric Disorders (WGSPD) (2017). Whole genome sequencing in psychiatric disorders: the WGSPD consortium. Nat. Neurosci. 20, 1661– 1668. [PubMed: 29184211] Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An JY, Peng M, Collins R, Grove J, Klei L, et al. (2020). Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism. Cell 180, 568–584.e23. [PubMed: 31981491] Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427. [PubMed: 25056061] Schmidt EM, Zhang J, Zhou W, Chen J, Mohlke KL, Chen YE, and Willer CJ (2015). GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31, 2601–2606. [PubMed: 25886982] Sestan N, and State MW (2018). Lost in Translation: Traversing the Complex Path from Genomics to Therapeutics in Autism Spectrum Disorder. Neuron 100, 406–423. [PubMed: 30359605] Shaw P, Gogtay N, and Rapoport J (2010). Childhood psychiatric disorders as anomalies in neurodevelopmental trajectories. Hum. Brain Mapp. 31, 917–925. [PubMed: 20496382] Silbereis JC, Pochareddy S, Zhu Y, Li M, and Sestan N (2016). The Cellular and Molecular Landscapes of the Developing Human Central Nervous System. Neuron 89, 248–268. [PubMed: 26796689] Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, and Tyers M (2006). BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539. [PubMed: 16381927] Tang SX, and Gur RE (2018). Longitudinal perspectives on the psychosis spectrum in 22q11.2 deletion syndrome. Am. J. Med. Genet. A. 176, 2192–2202. [PubMed: 29048724] Tau GZ, and Peterson BS (2010). Normal development of brain circuits. Neuropsychopharmacology 35, 147–168. [PubMed: 19794405] Turner TN, and Eichler EE (2019). The Role of De Novo Noncoding Regulatory Mutations in Neurodevelopmental Disorders. Trends Neurosci. 42, 115–127. [PubMed: 30563709] Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, and Kriegstein AR (2019). Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689. [PubMed: 31097668] Walker RL, Ramaswami G, Hartl C, Mancuso N, Gandal MJ, de la Torre-Ubieta L, Pasaniuc B, Stein JL, and Geschwind DH (2019). Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms. Cell 179, 750–771.e722. [PubMed: 31626773] Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, Clarke D, Gu M, Emani P, Yang YT, et al.; PsychENCODE Consortium (2018). Comprehensive functional genomic resource and integrative model for the human brain. Science 362, 6420. Werling DM, Brand H, An JY, Stone MR, Zhu L, Glessner JT, Collins RL, Dong S, Layer RM, Markenscoff-Papadimitriou E, et al. (2018). An analytical framework for whole-genome

Cell Rep. Author manuscript; available in PMC 2020 June 15. Werling et al. Page 34

sequence association studies and its implications for autism spectrum disorder. Nat. Genet. 50, Author ManuscriptAuthor Manuscript Author Manuscript Author 727–736. [PubMed: Manuscript Author 29700473] Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, et al.; Global Lipids Genetics Consortium (2013). Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283. [PubMed: 24097068] Willsey AJ, Sanders SJ, Li M, Dong S, Tebbenkamp AT, Muhle RA, Reilly SK, Lin L, Fertuzinhos S, Miller JA, et al. (2013). Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007. [PubMed: 24267886] Winter AG, Wildenhain J, and Tyers M (2011). BioGRID REST Service, BiogridPlugin2 and BioGRID WebGraph: new tools for access to interaction data at BioGRID. Bioinformatics 27, 1043–1044. [PubMed: 21300700] Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, Chu AY, Estrada K, Luan J, Kutalik Z, et al.; Electronic Medical Records and Genomics (eMEMERGEGE) Consortium; MIGen Consortium; PAGEGE Consortium; LifeLines Cohort Study (2014). Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186. [PubMed: 25282103] Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, Adams MJ, Agerbo E, Air TM, Andlauer TMF, et al.; eQTLGen; 23andMe; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681. [PubMed: 29700475] Xu X, Wells AB, O’Brien DR, Nehorai A, and Dougherty JD (2014). Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders. J. Neurosci. 34, 1420–1431. [PubMed: 24453331] Zhu Y, Sousa AMM, Gao T, Skarica M, Li M, Santpere G, Esteller-Cucala P, Juan D, Ferrá ndez-Peral L, Gulden FO, et al. (2018). Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, 362.

Cell Rep. Author manuscript; available in PMC 2020 June 15. Werling et al. Page 35

Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author Highligts • Whole-genome and RNA sequencing across human prefrontal cortex development

• Gene-specific developmental trajectories characterize the late-fetal transition • Identification of constant, prenatal-predominant, and postnatal-predominant eQTLs

• Integrated analysis implicates genes in loci associated with educational attainment

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Figure 1. Overview of the Dataset and the Analysis (A) 176 samples from the dorsolateral prefrontalcortex (DLFPC) of the developing human brain were processed to generate RNA-seq gene expression data and WGS data (top). The distribution of the samples is shown by sex (color) and developmental stage (x axis). Periods were defined previously (Kang et al., 2011), and epochs are defined as a superset of periods based on principal component analysis of these RNA-seq data (Figure 2). (B) Analyses conducted using these data. Thewidth of each box corresponds to the samples included in each analysis. See also Table S1 and Figure S1.

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Figure 2. Temporal Trajectories of Gene Expression in the Human DLPFC (A) Gene expression log base 2 counts per million (log2CPM) for each sample was used to calculate principal components (Figure S2). The first principal component (PC1) explains 42% of the variance between samples, and 81% of variance in PC1 is explained by developmental stage (Figure S2). The changes in PC1 over time were used to define four “epochs” of gene expression. Dotted lines represent the boundaries of the indicated developmental period as defined previously (Kang et al., 2011).

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(B and C) Trajectory analysis identifies three sets of genes with similar developmental Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author profiles across the late-fetal transition in epoch 2 (B; Table S2). For each group, the expression over time, normalized by the interquartile range and locally estimated scatterplot smoothing (LOESS), is shown as a line, with the narrow 95% CI in gray. These three groups are further characterized by plotting (C) the median log2CPM across all samples in epoch 1 and epoch 3, with the difference for each gene shown as a line. (D) The relative proportion of Gencode protein-coding and noncoding genes with gene counts. (E) The distribution of probability loss-of-function intolerance (pLI) scores for protein- coding genes (Lek et al., 2016) with gene counts. (F) Enrichment in the most tissue-specific genes from the 53 tissues with bulk tissue RNA- seq data from the Genotype-Tissue Expression Consortium (GTEx) (GTEx Consortium, 2015). (G) Pattern of expression for ten cell type-specific genes (Table S2) for each of five neuronal lineage cell types (LOESS with 95% CI). (H) Analysis in (G) repeated for five glial lineage cell types. OPC, oligodendrocyte progenitor cell. Statistical analyses: (A) principal component analysis; (B) longitudinal mixture model with Gaussian noise; (D) FET; (E) two-sided WRST; (F) t-test. See also Table S2 and Figures S1 and S2.

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Figure 3. Co-expression Modules in the Developing Human Cortex (A) Weighted genome co-expression network analysis (WGCNA) identified 19 modules comprised of 10,459 of 23,782 expressed genes. Modules are shown ascolored nodes plotted based on the first two dimensions from multidimensional scaling. The weight of the connecting lines (edges) represents the degree of correlation between module eigengenes. (B) LOESS expression values across development are shown with 95% CIs for the 19 modules arranged in five groups based on proximaity in (A) and similartemporal trajectories.

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(C) Gene Ontology enrichment analysis for each module, showing only biological processes Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author with the lowest false discovery rate (FDR). (D) Mosaic plot showing the relationship between the five groups of co-expression modules (from A) and genes with falling, rising, or non-transitional temporaltrajectories (Figure 2). The area is proportional to the number of genes in each bin. Detailed relationships between modules and temporal trajectories are shown in Figure S3. (E) Enrichment between the 19 modules and the 200 genes most specific to 19 cell type clusters defined by single-cell RNA-seq data in the developing humancortex (Nowakowski et al., 2017). (F) Enrichment between the 19 modules and the 200 genes most specific to 29 cell type clusters defined by single nucleus RNA-seq data in the adult humanDLPFC (Li et al., 2018). (G) Module preservation in independent BrainSpan samples (Li et al., 2018) from the same brain region (left), other cortical regions (center), and five subcortical regions (right). SRP, signal recognition particle; C, cluster of single nuclei; L, cortical layer; ND, layer not defined; RG, radial glia; IPC, intermediate progenitor cell; NN, newborn neuron; ExN, excitatory neuron; InN, inhibitory neuron; CGE, caudal ganglionic eminence; MGE, medial ganglionic eminence. Statistical analysis: (A) WGCNA with consensus module detection from 100 random resamplings; (C) FET, corrected for gProfiler Gene Ontology pathways (10–2,000 term size); (E) FET corrected for 361 comparisons; (F) FET corrected for 551 comparisons; (G) FET corrected for 19 comparisons. See also Table S2 and S3 and Figure S3.

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Figure 4. Expression of Genes Associated with CNS Traits and Disorders (A) Genes from genome-wide significant loci were collated for ten CNS traits and disorders from exome sequencing or genome-wide association studies (GWASs). The enrichment is shown for the three trajectory groups (Figure 2). (B) The analysis in (A) repeated for co-expression modules. (C) The analysis in (A) repeated for genes enriched for cell type clusters from single-cell RNA-seq of the prenatal human brain. Statistical analysis: (A) FET corrected for 30 comparisons; (B) FET corrected for 190 comparisons; (C) FET corrected for 190 comparisons. See also Table S2 and S4, and Figure S4.

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Figure 5. Common Variant cis-eQTLs (A) Effects of the top expression quantitative trait locus (eQTL) per gene regulated by an eQTL (eGene) with FDR ≤ 0.05 in the BrainVar analyses (x axis, union of results from complete sample, prenatal-only, and postnatal-only analyses) versus effects observed in the prenatal whole brain (O’Brien et al., 2018) (left) and postnatal frontal cortex (Aguet et al., 2017) (right, y axis).

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(B) Prenatal (x axis) and postnatal (y axis) effects for the eQTLs with the smallest p value Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author for 8,421 eGenes (points). The eQTLs are split into five categories basedon temporal predominance using effect size and statistical thresholds; categories are represented by color. (C) Effects of the top eQTL per eGene with FDR ≤ 0.05 from the prenatal-predominant (red) or postnatal-predominant (blue) eQTL categories from BrainVar (x axis) versus effects observed in the published datasets described in (A) (y axis). (D) Density plot of the distance of top eQTLs per eGene from the transcription start site by eGene temporal category. (E–G) Characteristics of non-eGenes, temporal-predominant eGenes, and disorder- associated genes are shown by plotting the (E) proportion of coding and noncoding genes, (F) proportion of genes with pLI scores in different bins, and (G) BioGRID protein-protein interactions (permuted Z scores from Ripley’s K-net function; Cornish and Markowetz, 2014; the black line is the non-eGene median). (H) Mosaic plot of the proportion of genes in each temporal trajectory with eGenes split by temporal category. (I) Expression data binned by genotype for the top prenatal-predominant eQTL for CHD1L, a gene with a rising trajectory. Main panel: gene expression by sample age across development. Lines represent LOESS trajectories for expression in samples with each of three genotypes for rs138586968. Inset: boxplots for prenatal (left) and postnatal (right) samples with each of three rs138586968 genotypes. (J) Distribution of eQTL effect size for eGenes binned by pLI scores. The black line represents the median of the transcripts with no pLI score. (K) Distributions of between-sample variance in the expression level of expressed genes binned by pLI scores. The black line represents the median variance ofthe transcripts with no pLI score. TSS, transcription start site; Statistical analysis: (A) and (C) Pearson correlation; (D) and (G), two-sided WRST test for constant versus other eGenes; (E) and (F), two-sided FET for constant versus other eGenes; (J) and (K), two-sided WRST test for each of four pLI bins versus genes with no pLI score. See also Tables S2 and S5 and Figure S5.

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Figure 6. Colocalization of Two eQTLs with Educational Attainment GWAS Loci (A) Statistical evidence of association with educational attainment for SNPs (points) alongside the recombination rate (blue line). Color shows the correlation withthe SNP rs1043209 (Pruim et al., 2010). (B) The statistical evidence for the SNP rs1043209 being an eQTL is shown for each gene within proximity of the locus. No other genes had high posteriorprobabilities for colocalization. (C) The statistical evidence for being an eQTL for the noncoding RNA LOC101926933 (x axis) is shown against the evidence for association with educational attainment (y axis) for each SNP (points). (D) The expression of LOC101926933 is shown for each sample across development with genotype at rs1043209, indicated by color. (E–H) Another educational attainment locus that colocalized with the gene RHEBL1 in the prenatal period only is shown, as in (A)–(D).

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Statistical analysis: (B) colocalization analysis; (D) inset, ANOVA; (E) colocalization Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author analysis; (H) inset, ANOVA. See also Tables S2 and S6 and Figure S6.

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KEY RESOURCES TABLE Author ManuscriptAuthor Manuscript Author Manuscript Author Manuscript Author REAGENT or RESOURCE SOURCE IDENTIFIER Biological Samples See Table S1 for a list of human post-mortem N/A tissue included in the study. Chemicals, Peptides, and Recombinant Proteins Agencourt AMPure XP beads Beckman Coulter A63881 RNAClean XP beads Beckman Coulter A63987 RNA ScreenTape Agilent 5067–5576 RNA ScreenTape Sample Buffer Agilent 5067–5577 D1000 ScreenTape Agilent 5067–5582 D1000 Sample Buffer Agilent 5067–5602 Stainless Steel Beads 0.9 – 2.0 mm blend Next Advance SSB14B Critical Commercial Assays mirVana miRNA Isolation Kit, with phenol Thermo Fisher Scientific AM1560 TURBO DNA-free Kit Thermo Fisher Scientific AM1907 TruSeq Stranded Total RNA HT Sample Prep Illumina 20020599 Kit with Ribo-Zero Gold kit QIAamp DNA Mini Kit (250) QIAGEN 51306 Deposited Data Raw WGS data https://www.synapse.org/#! syn21557948 Synapse:syn4921369 Processed WGS data https://www.synapse.org/#! syn21557948 Synapse:syn4921369 Raw RNA-seq data https://www.synapse.org/#! syn21557948 Synapse:syn4921369 Processed RNA-seq data https://www.synapse.org/#! syn21557948 Synapse:syn4921369 Software and Algorithms HTSeq v.0.6.0. Anders et al., 2015 https://htseq.readthedocs.io/en/master/ STAR v.2.4.2a Dobin et al., 2013 https://github.com/alexdobin/STAR limma v.3.36.5 Ritchie et al., 2015 http://bioconductor.org/packages/release/bioc/ html/limma.html HCP Mostafavi et al., 2013 https://github.com/mvaniterson/Rhcpp SVA v. 3.28.0 Leek et al., 2012 https://www.bioconductor.org/packages/release/ bioc/html/sva.html rsq v.1.1 Zhang Lab, Purdue https://cran.r-project.org/web/packages/rsq/ index.html igraph v.1.2.2 RStudio, Inc., Boston https://cran.r-project.org/web/packages/igraph/ index.html SANTA v.2.14.0 Cornish and Markowetz, 2014 https://bioconductor.org/packages/release/bioc/ html/SANTA.html WGCNA v 1.63 Langfelder and Horvath, 2008 https://horvath.genetics.ucla.edu/html/ CoexpressionNetwork/Rpackages/WGCNA/ BWA v0.7.15 Li and Durbin, 2009 https://github.com/lh3/bwa/releases

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REAGENT or RESOURCE SOURCE IDENTIFIER Picard v2.17.5 for sorting & removing duplicate Broad Institute, Boston https://github.com/broadinstitute/picard/ Author ManuscriptAuthor reads, Manuscript Author v2.18.1 for checking Manuscript Author sample sequencing Manuscript Author depth GenomeAnalysisToolKit GATK v3.8-0- McKenna et al., 2010 https://github.com/broadgsa/gatk ge9d806836 Sentieon v201711.01 Freed et al., 2017 https://www.sentieon.com/products/ VerifyBamId v1.1.3 Jun et al., 2012 https://github.com/statgen/verifyBamID/ Peddy v0.3.2 Pedersen and Quinlan, 2017 https://github.com/brentp/peddy Hail v0.1 Github https://github.com/hail-is/hail coloc Giambartolomei et al., 2014 http://cran.r-project.org/web/packages/coloc MAGMA v1.07b de Leeuw et al., 2015 https://ctg.cncr.nl/software/magma gProfileR v0.6.7 Reimand et al., 2011 https://github.com/cran/gProfileR/releases GREGOR Schmidt et al., 2015 http://csg.sph.umich.edu/GREGOR/index.php/ site/download Whole-genome analysis pipeline Sanders Lab, UCSF https://github.com/sanderslab/psychcore- compute-platform Other GENCODE annotation v21 (GRCh38) Harrow et al., 2012 https://www.gencodegenes.org/human/ release_21.html HUGO Gene Nomenclature Committee European Bioinformatics Institute, ftp://ftp.ebi.ac.uk/pub/databases/ Complete dataset (2018) Cambridge, UK genenames/new/tsv/hgnc_complete_set.txt BioGRID (v3.4.132) Stark et al., 2006 https://downloads.thebiogrid.org/BioGRID/ Release-Archive/BIOGRID-3.4.132/ ENSEMBLE VEP (v90) McLaren et al., 2016 https://github.com/Ensembl/ensembl-vep/ releases

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